{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "from sympy import Symbol, symbols, Matrix, sin, cos\n",
    "from sympy import init_printing\n",
    "init_printing(use_latex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Extended Kalman Filter Implementation for Constant Turn Rate and Velocity (CTRV) Vehicle Model in Python\n",
    "\n",
    "![Extended Kalman Filter Step](Extended-Kalman-Filter-Step.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "[Wikipedia](http://en.wikipedia.org/wiki/Extended_Kalman_filter) writes: In the extended Kalman filter, the state transition and observation models need not be linear functions of the state but may instead be differentiable functions.\n",
    "\n",
    "$\\boldsymbol{x}_{k} = g(\\boldsymbol{x}_{k-1}, \\boldsymbol{u}_{k-1}) + \\boldsymbol{w}_{k-1}$\n",
    "\n",
    "$\\boldsymbol{z}_{k} = h(\\boldsymbol{x}_{k}) + \\boldsymbol{v}_{k}$\n",
    "\n",
    "Where $w_k$ and $v_k$ are the process and observation noises which are both assumed to be zero mean Multivariate Gaussian noises with covariance matrix $Q$ and $R$ respectively.\n",
    "\n",
    "The function $g$ can be used to compute the predicted state from the previous estimate and similarly the function $h$ can be used to compute the predicted measurement from the predicted state. However, $g$ and $h$ cannot be applied to the covariance directly. Instead a matrix of partial derivatives (the Jacobian matrix) is computed.\n",
    "\n",
    "At each time step, the Jacobian is evaluated with current predicted states. These matrices can be used in the Kalman filter equations. This process essentially linearizes the non-linear function around the current estimate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## State Vector - Constant Turn Rate and Velocity Vehicle Model (CTRV)\n",
    "\n",
    "Situation covered: You have a velocity sensor, which measures the vehicle speed ($v$) in heading direction ($\\psi$) and a yaw rate sensor ($\\dot \\psi$) which both have to fused with the position ($x$ & $y$) from a GPS sensor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Constant Turn Rate, Constant Velocity Model for a vehicle ![CTRV Model](CTRV-Model.png)\n",
    "\n",
    "$$x_k= \\left[ \\matrix{ x \\\\ y \\\\ \\psi \\\\ v \\\\ \\dot\\psi} \\right] = \\left[ \\matrix{ \\text{Position X} \\\\ \\text{Position Y} \\\\ \\text{Heading} \\\\ \\text{Velocity} \\\\ \\text{Yaw Rate}} \\right]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "numstates=5 # States"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We have different frequency of sensor readings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "dt = 1.0/50.0 # Sample Rate of the Measurements is 50Hz\n",
    "dtGPS=1.0/10.0 # Sample Rate of GPS is 10Hz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Developing the math behind dynamic model\n",
    "\n",
    "All symbolic calculations are made with [Sympy](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-5-Sympy.ipynb). Thanks!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "vs, psis, dpsis, dts, xs, ys, lats, lons = symbols('v \\psi \\dot\\psi T x y lat lon')\n",
    "\n",
    "gs = Matrix([[xs+(vs/dpsis)*(sin(psis+dpsis*dts)-sin(psis))],\n",
    "             [ys+(vs/dpsis)*(-cos(psis+dpsis*dts)+cos(psis))],\n",
    "             [psis+dpsis*dts],\n",
    "             [vs],\n",
    "             [dpsis]])\n",
    "state = Matrix([xs,ys,psis,vs,dpsis])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Dynamic Function $g$\n",
    "\n",
    "This formulas calculate how the state is evolving from one to the next time step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\left[\\begin{matrix}x + \\frac{v}{\\dot\\psi} \\left(- \\sin{\\left (\\psi \\right )} + \\sin{\\left (T \\dot\\psi + \\psi \\right )}\\right)\\\\y + \\frac{v}{\\dot\\psi} \\left(\\cos{\\left (\\psi \\right )} - \\cos{\\left (T \\dot\\psi + \\psi \\right )}\\right)\\\\T \\dot\\psi + \\psi\\\\v\\\\\\dot\\psi\\end{matrix}\\right]$$"
      ],
      "text/plain": [
       "⎡    v⋅(-sin(\\psi) + sin(T⋅\\dot\\psi + \\psi))⎤\n",
       "⎢x + ───────────────────────────────────────⎥\n",
       "⎢                    \\dot\\psi               ⎥\n",
       "⎢                                           ⎥\n",
       "⎢    v⋅(cos(\\psi) - cos(T⋅\\dot\\psi + \\psi)) ⎥\n",
       "⎢y + ────────────────────────────────────── ⎥\n",
       "⎢                   \\dot\\psi                ⎥\n",
       "⎢                                           ⎥\n",
       "⎢             T⋅\\dot\\psi + \\psi             ⎥\n",
       "⎢                                           ⎥\n",
       "⎢                     v                     ⎥\n",
       "⎢                                           ⎥\n",
       "⎣                 \\dot\\psi                  ⎦"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Calculate the Jacobian of the Dynamic function $g$ with respect to the state vector $x$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\left[\\begin{matrix}x\\\\y\\\\\\psi\\\\v\\\\\\dot\\psi\\end{matrix}\\right]$$"
      ],
      "text/plain": [
       "⎡   x    ⎤\n",
       "⎢        ⎥\n",
       "⎢   y    ⎥\n",
       "⎢        ⎥\n",
       "⎢  \\psi  ⎥\n",
       "⎢        ⎥\n",
       "⎢   v    ⎥\n",
       "⎢        ⎥\n",
       "⎣\\dot\\psi⎦"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\left[\\begin{matrix}1 & 0 & \\frac{v}{\\dot\\psi} \\left(- \\cos{\\left (\\psi \\right )} + \\cos{\\left (T \\dot\\psi + \\psi \\right )}\\right) & \\frac{1}{\\dot\\psi} \\left(- \\sin{\\left (\\psi \\right )} + \\sin{\\left (T \\dot\\psi + \\psi \\right )}\\right) & \\frac{T v}{\\dot\\psi} \\cos{\\left (T \\dot\\psi + \\psi \\right )} - \\frac{v}{\\dot\\psi^{2}} \\left(- \\sin{\\left (\\psi \\right )} + \\sin{\\left (T \\dot\\psi + \\psi \\right )}\\right)\\\\0 & 1 & \\frac{v}{\\dot\\psi} \\left(- \\sin{\\left (\\psi \\right )} + \\sin{\\left (T \\dot\\psi + \\psi \\right )}\\right) & \\frac{1}{\\dot\\psi} \\left(\\cos{\\left (\\psi \\right )} - \\cos{\\left (T \\dot\\psi + \\psi \\right )}\\right) & \\frac{T v}{\\dot\\psi} \\sin{\\left (T \\dot\\psi + \\psi \\right )} - \\frac{v}{\\dot\\psi^{2}} \\left(\\cos{\\left (\\psi \\right )} - \\cos{\\left (T \\dot\\psi + \\psi \\right )}\\right)\\\\0 & 0 & 1 & 0 & T\\\\0 & 0 & 0 & 1 & 0\\\\0 & 0 & 0 & 0 & 1\\end{matrix}\\right]$$"
      ],
      "text/plain": [
       "⎡      v⋅(-cos(\\psi) + cos(T⋅\\dot\\psi + \\psi))  -sin(\\psi) + sin(T⋅\\dot\\psi + \n",
       "⎢1  0  ───────────────────────────────────────  ──────────────────────────────\n",
       "⎢                      \\dot\\psi                               \\dot\\psi        \n",
       "⎢                                                                             \n",
       "⎢                                                                             \n",
       "⎢      v⋅(-sin(\\psi) + sin(T⋅\\dot\\psi + \\psi))  cos(\\psi) - cos(T⋅\\dot\\psi + \\\n",
       "⎢0  1  ───────────────────────────────────────  ──────────────────────────────\n",
       "⎢                      \\dot\\psi                              \\dot\\psi         \n",
       "⎢                                                                             \n",
       "⎢                                                                             \n",
       "⎢0  0                     1                                      0            \n",
       "⎢                                                                             \n",
       "⎢0  0                     0                                      1            \n",
       "⎢                                                                             \n",
       "⎣0  0                     0                                      0            \n",
       "\n",
       "\\psi)  T⋅v⋅cos(T⋅\\dot\\psi + \\psi)   v⋅(-sin(\\psi) + sin(T⋅\\dot\\psi + \\psi))⎤\n",
       "─────  ────────────────────────── - ───────────────────────────────────────⎥\n",
       "                \\dot\\psi                                   2               ⎥\n",
       "                                                   \\dot\\psi                ⎥\n",
       "                                                                           ⎥\n",
       "psi)   T⋅v⋅sin(T⋅\\dot\\psi + \\psi)   v⋅(cos(\\psi) - cos(T⋅\\dot\\psi + \\psi)) ⎥\n",
       "────   ────────────────────────── - ────────────────────────────────────── ⎥\n",
       "                \\dot\\psi                                  2                ⎥\n",
       "                                                  \\dot\\psi                 ⎥\n",
       "                                                                           ⎥\n",
       "                                        T                                  ⎥\n",
       "                                                                           ⎥\n",
       "                                        0                                  ⎥\n",
       "                                                                           ⎥\n",
       "                                        1                                  ⎦"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.jacobian(state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "It has to be computed on every filter step because it consists of state variables!\n",
    "\n",
    "To Sympy Team: A `.to_python` and `.to_c` and `.to_matlab` whould be nice to generate code, like it already works with `print latex()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Initial Uncertainty $P_0$\n",
    "\n",
    "Initialized with $0$ means you are pretty sure where the vehicle starts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1000.    0.    0.    0.    0.]\n",
      " [   0. 1000.    0.    0.    0.]\n",
      " [   0.    0. 1000.    0.    0.]\n",
      " [   0.    0.    0. 1000.    0.]\n",
      " [   0.    0.    0.    0. 1000.]] (5, 5)\n"
     ]
    }
   ],
   "source": [
    "P = np.diag([1000.0, 1000.0, 1000.0, 1000.0, 1000.0])\n",
    "print(P, P.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "im = plt.imshow(P, interpolation=\"none\", cmap=plt.get_cmap('binary'))\n",
    "plt.title('Initial Covariance Matrix $P$')\n",
    "ylocs, ylabels = plt.yticks()\n",
    "# set the locations of the yticks\n",
    "plt.yticks(np.arange(6))\n",
    "# set the locations and labels of the yticks\n",
    "plt.yticks(np.arange(5),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "xlocs, xlabels = plt.xticks()\n",
    "# set the locations of the yticks\n",
    "plt.xticks(np.arange(6))\n",
    "# set the locations and labels of the yticks\n",
    "plt.xticks(np.arange(5),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "plt.xlim([-0.5,4.5])\n",
    "plt.ylim([4.5, -0.5])\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "divider = make_axes_locatable(plt.gca())\n",
    "cax = divider.append_axes(\"right\", \"5%\", pad=\"3%\")\n",
    "plt.colorbar(im, cax=cax)\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Process Noise Covariance Matrix $Q$\n",
    "\n",
    "\"*The state uncertainty model models the disturbances which excite the linear system. Conceptually, it estimates how bad things can get when the system is run open loop for a given period of time.*\" - Kelly, A. (1994). A 3D state space formulation of a navigation Kalman filter for autonomous vehicles, (May). Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA282853"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.0976e-06 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 3.0976e-06 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 4.0000e-06 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 0.0000e+00 3.0976e-02 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 4.0000e-04]] (5, 5)\n"
     ]
    }
   ],
   "source": [
    "sGPS     = 0.5*8.8*dt**2  # assume 8.8m/s2 as maximum acceleration, forcing the vehicle\n",
    "sCourse  = 0.1*dt # assume 0.1rad/s as maximum turn rate for the vehicle\n",
    "sVelocity= 8.8*dt # assume 8.8m/s2 as maximum acceleration, forcing the vehicle\n",
    "sYaw     = 1.0*dt # assume 1.0rad/s2 as the maximum turn rate acceleration for the vehicle\n",
    "\n",
    "Q = np.diag([sGPS**2, sGPS**2, sCourse**2, sVelocity**2, sYaw**2])\n",
    "print(Q, Q.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "im = plt.imshow(Q, interpolation=\"none\", cmap=plt.get_cmap('binary'))\n",
    "plt.title('Process Noise Covariance Matrix $Q$')\n",
    "ylocs, ylabels = plt.yticks()\n",
    "# set the locations of the yticks\n",
    "plt.yticks(np.arange(8))\n",
    "# set the locations and labels of the yticks\n",
    "plt.yticks(np.arange(7),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "xlocs, xlabels = plt.xticks()\n",
    "# set the locations of the yticks\n",
    "plt.xticks(np.arange(8))\n",
    "# set the locations and labels of the yticks\n",
    "plt.xticks(np.arange(7),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "plt.xlim([-0.5,4.5])\n",
    "plt.ylim([4.5, -0.5])\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "divider = make_axes_locatable(plt.gca())\n",
    "cax = divider.append_axes(\"right\", \"5%\", pad=\"3%\")\n",
    "plt.colorbar(im, cax=cax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Real Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "strptime() argument 0 must be str, not <class 'bytes'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-1c23dc539f4f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m                   converters={1: mdates.strpdate2num('%H%M%S%f'),\n\u001b[1;32m      6\u001b[0m                               0: mdates.strpdate2num('%y%m%d')},\n\u001b[0;32m----> 7\u001b[0;31m                   skiprows=0)\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Read \\'%s\\' successfully.'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdatafile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding)\u001b[0m\n\u001b[1;32m   1090\u001b[0m         \u001b[0;31m# converting the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1092\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mread_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_loadtxt_chunksize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1093\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1094\u001b[0m                 \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mread_data\u001b[0;34m(chunk_size)\u001b[0m\n\u001b[1;32m   1017\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1018\u001b[0m             \u001b[0;31m# Convert each value according to its column and store\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1019\u001b[0;31m             \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mconv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconverters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1020\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m             \u001b[0;31m# Then pack it according to the dtype's nesting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1017\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1018\u001b[0m             \u001b[0;31m# Convert each value according to its column and store\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1019\u001b[0;31m             \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mconv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconverters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1020\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m             \u001b[0;31m# Then pack it according to the dtype's nesting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mtobytes_first\u001b[0;34m(x, conv)\u001b[0m\n\u001b[1;32m   1076\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1077\u001b[0m                         \u001b[0;32mreturn\u001b[0m \u001b[0mconv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1078\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mconv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"latin1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1079\u001b[0m                 \u001b[0;32mimport\u001b[0m \u001b[0mfunctools\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m                 \u001b[0mconverters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtobytes_first\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, s)\u001b[0m\n\u001b[1;32m    358\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ma\u001b[0m \u001b[0mdate2num\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m         \"\"\"\n\u001b[0;32m--> 360\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mdate2num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/_strptime.py\u001b[0m in \u001b[0;36m_strptime_time\u001b[0;34m(data_string, format)\u001b[0m\n\u001b[1;32m    557\u001b[0m     \"\"\"Return a time struct based on the input string and the\n\u001b[1;32m    558\u001b[0m     format string.\"\"\"\n\u001b[0;32m--> 559\u001b[0;31m     \u001b[0mtt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_strptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_string\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    560\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstruct_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_STRUCT_TM_ITEMS\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/_strptime.py\u001b[0m in \u001b[0;36m_strptime\u001b[0;34m(data_string, format)\u001b[0m\n\u001b[1;32m    327\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"strptime() argument {} must be str, not {}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 329\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_TimeRE_cache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_regex_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: strptime() argument 0 must be str, not <class 'bytes'>"
     ]
    }
   ],
   "source": [
    "#path = './../RaspberryPi-CarPC/TinkerDataLogger/DataLogs/2014/'\n",
    "datafile = '2014-03-26-000-Data.csv'\n",
    "\n",
    "date, \\\n",
    "time, \\\n",
    "millis, \\\n",
    "ax, \\\n",
    "ay, \\\n",
    "az, \\\n",
    "rollrate, \\\n",
    "pitchrate, \\\n",
    "yawrate, \\\n",
    "roll, \\\n",
    "pitch, \\\n",
    "yaw, \\\n",
    "speed, \\\n",
    "course, \\\n",
    "latitude, \\\n",
    "longitude, \\\n",
    "altitude, \\\n",
    "pdop, \\\n",
    "hdop, \\\n",
    "vdop, \\\n",
    "epe, \\\n",
    "fix, \\\n",
    "satellites_view, \\\n",
    "satellites_used, \\\n",
    "temp = np.loadtxt(datafile, delimiter=',', unpack=True, \n",
    "                  converters={1: mdates.strpdate2num('%H%M%S%f'),\n",
    "                              0: mdates.strpdate2num('%y%m%d')},\n",
    "                  skiprows=1)\n",
    "\n",
    "print('Read \\'%s\\' successfully.' % datafile)\n",
    "\n",
    "# A course of 0° means the Car is traveling north bound\n",
    "# and 90° means it is traveling east bound.\n",
    "# In the Calculation following, East is Zero and North is 90°\n",
    "# We need an offset.\n",
    "course =(-course+90.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "file = pd.read_csv('2014-03-26-000-Data.csv')\n",
    "df = pd.DataFrame(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Measurement Function $h$\n",
    "\n",
    "Matrix $J_H$ is the Jacobian of the Measurement function $h$ with respect to the state. Function $h$ can be used to compute the predicted measurement from the predicted state.\n",
    "\n",
    "If a GPS measurement is available, the following function maps the state to the measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\left[\\begin{matrix}x\\\\y\\\\v\\\\\\dot\\psi\\end{matrix}\\right]$$"
      ],
      "text/plain": [
       "⎡   x    ⎤\n",
       "⎢        ⎥\n",
       "⎢   y    ⎥\n",
       "⎢        ⎥\n",
       "⎢   v    ⎥\n",
       "⎢        ⎥\n",
       "⎣\\dot\\psi⎦"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs = Matrix([[xs],\n",
    "             [ys],\n",
    "             [vs],\n",
    "             [dpsis]])\n",
    "hs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\\left[\\begin{matrix}1 & 0 & 0 & 0 & 0\\\\0 & 1 & 0 & 0 & 0\\\\0 & 0 & 0 & 1 & 0\\\\0 & 0 & 0 & 0 & 1\\end{matrix}\\right]$$"
      ],
      "text/plain": [
       "⎡1  0  0  0  0⎤\n",
       "⎢             ⎥\n",
       "⎢0  1  0  0  0⎥\n",
       "⎢             ⎥\n",
       "⎢0  0  0  1  0⎥\n",
       "⎢             ⎥\n",
       "⎣0  0  0  0  1⎦"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JHs=hs.jacobian(state)\n",
    "JHs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "If no GPS measurement is available, simply set the corresponding values in $J_h$ to zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Measurement Noise Covariance $R$\n",
    "\n",
    "\"In practical use, the uncertainty estimates take on the significance of relative weights of state estimates and measurements. So it is not so much important that uncertainty is absolutely correct as it is that it be relatively consistent across all models\" - Kelly, A. (1994). A 3D state space formulation of a navigation Kalman filter for autonomous vehicles, (May). Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA282853"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.6e+01 0.0e+00 0.0e+00 0.0e+00]\n",
      " [0.0e+00 3.6e+01 0.0e+00 0.0e+00]\n",
      " [0.0e+00 0.0e+00 1.0e+00 0.0e+00]\n",
      " [0.0e+00 0.0e+00 0.0e+00 1.0e-02]] (4, 4)\n"
     ]
    }
   ],
   "source": [
    "varGPS = 6.0 # Standard Deviation of GPS Measurement\n",
    "varspeed = 1.0 # Variance of the speed measurement\n",
    "varyaw = 0.1 # Variance of the yawrate measurement\n",
    "R = np.matrix([[varGPS**2, 0.0, 0.0, 0.0],\n",
    "               [0.0, varGPS**2, 0.0, 0.0],\n",
    "               [0.0, 0.0, varspeed**2, 0.0],\n",
    "               [0.0, 0.0, 0.0, varyaw**2]])\n",
    "\n",
    "print(R, R.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x324 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4.5, 4.5))\n",
    "im = plt.imshow(R, interpolation=\"none\", cmap=plt.get_cmap('binary'))\n",
    "plt.title('Measurement Noise Covariance Matrix $R$')\n",
    "ylocs, ylabels = plt.yticks()\n",
    "# set the locations of the yticks\n",
    "plt.yticks(np.arange(5))\n",
    "# set the locations and labels of the yticks\n",
    "plt.yticks(np.arange(4),('$x$', '$y$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "xlocs, xlabels = plt.xticks()\n",
    "# set the locations of the yticks\n",
    "plt.xticks(np.arange(5))\n",
    "# set the locations and labels of the yticks\n",
    "plt.xticks(np.arange(4),('$x$', '$y$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "plt.xlim([-0.5,3.5])\n",
    "plt.ylim([3.5, -0.5])\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "divider = make_axes_locatable(plt.gca())\n",
    "cax = divider.append_axes(\"right\", \"5%\", pad=\"3%\")\n",
    "plt.colorbar(im, cax=cax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Identity Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 1.]] (5, 5)\n"
     ]
    }
   ],
   "source": [
    "I = np.eye(numstates)\n",
    "print(I, I.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Approx. Lat/Lon to Meters to check Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "altitude = df.altitude\n",
    "latitude = df.latitude\n",
    "longitude = df.longitude\n",
    "course = df.course\n",
    "speed = df.course\n",
    "yawrate = df.course"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "RadiusEarth = 6378388.0 # m\n",
    "arc= 2.0*np.pi*(RadiusEarth+altitude)/360.0 # m/°\n",
    "\n",
    "dx = arc * np.cos(latitude*np.pi/180.0) * np.hstack((0.0, np.diff(longitude))) # in m\n",
    "dy = arc * np.hstack((0.0, np.diff(latitude))) # in m\n",
    "\n",
    "mx = np.cumsum(dx)\n",
    "my = np.cumsum(dy)\n",
    "\n",
    "ds = np.sqrt(dx**2+dy**2)\n",
    "\n",
    "GPS=(ds!=0.0).astype('bool') # GPS Trigger for Kalman Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Initial State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.        ]\n",
      " [ 0.        ]\n",
      " [ 5.65835743]\n",
      " [90.05655556]\n",
      " [ 5.65835743]] (5, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/latex": [
       "$$\\left ( -0.0018070489844683505, \\quad 0.0018070489844683366, \\quad -0.0027823767476085302, \\quad 0.002782376747608544\\right )$$"
      ],
      "text/plain": [
       "(-0.0018070489844683505, 0.0018070489844683366, -0.0027823767476085302, 0.0027\n",
       "82376747608544)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.matrix([[mx[0], my[0], course[0]/180.0*np.pi, speed[0]/3.6+0.001, yawrate[0]/180.0*np.pi]]).T\n",
    "print(x, x.shape)\n",
    "\n",
    "U=float(np.cos(x[2])*x[3])\n",
    "V=float(np.sin(x[2])*x[3])\n",
    "\n",
    "plt.quiver(x[0], x[1], U, V)\n",
    "plt.scatter(float(x[0]), float(x[1]), s=100)\n",
    "plt.title('Initial Location')\n",
    "plt.axis('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Put everything together as a measurement vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 10800)\n"
     ]
    }
   ],
   "source": [
    "measurements = np.vstack((mx, my, speed/3.6, yawrate/180.0*np.pi))\n",
    "# Lenth of the measurement\n",
    "m = measurements.shape[1]\n",
    "print(measurements.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "file = pd.read_csv('2014-03-26-000-Data.csv')\n",
    "df = pd.DataFrame(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Preallocation for Plotting\n",
    "x0 = []\n",
    "x1 = []\n",
    "x2 = []\n",
    "x3 = []\n",
    "x4 = []\n",
    "x5 = []\n",
    "Zx = []\n",
    "Zy = []\n",
    "Px = []\n",
    "Py = []\n",
    "Pdx= []\n",
    "Pdy= []\n",
    "Pddx=[]\n",
    "Pddy=[]\n",
    "Kx = []\n",
    "Ky = []\n",
    "Kdx= []\n",
    "Kdy= []\n",
    "Kddx=[]\n",
    "dstate=[]\n",
    "\n",
    "\n",
    "def savestates(x, Z, P, K):\n",
    "    x0.append(float(x[0]))\n",
    "    x1.append(float(x[1]))\n",
    "    x2.append(float(x[2]))\n",
    "    x3.append(float(x[3]))\n",
    "    x4.append(float(x[4]))\n",
    "    Zx.append(float(Z[0]))\n",
    "    Zy.append(float(Z[1]))    \n",
    "    Px.append(float(P[0,0]))\n",
    "    Py.append(float(P[1,1]))\n",
    "    Pdx.append(float(P[2,2]))\n",
    "    Pdy.append(float(P[3,3]))\n",
    "    Pddx.append(float(P[4,4]))\n",
    "    Kx.append(float(K[0,0]))\n",
    "    Ky.append(float(K[1,0]))\n",
    "    Kdx.append(float(K[2,0]))\n",
    "    Kdy.append(float(K[3,0]))\n",
    "    Kddx.append(float(K[4,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Extended Kalman Filter\n",
    "\n",
    "![Extended Kalman Filter Step](Extended-Kalman-Filter-Step.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "$$x_k= \\begin{bmatrix} x \\\\ y \\\\ \\psi \\\\ v \\\\ \\dot\\psi \\end{bmatrix} = \\begin{bmatrix} \\text{Position X} \\\\ \\text{Position Y} \\\\ \\text{Heading} \\\\ \\text{Velocity} \\\\ \\text{Yaw Rate} \\end{bmatrix} =  \\underbrace{\\begin{matrix}x[0] \\\\ x[1] \\\\ x[2] \\\\ x[3] \\\\ x[4]  \\end{matrix}}_{\\textrm{Python Nomenclature}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "for filterstep in range(m):\n",
    "\n",
    "    # Time Update (Prediction)\n",
    "    # ========================\n",
    "    # Project the state ahead\n",
    "    # see \"Dynamic Matrix\"\n",
    "    if np.abs(yawrate[filterstep])<0.0001: # Driving straight\n",
    "        x[0] = x[0] + x[3]*dt * np.cos(x[2])\n",
    "        x[1] = x[1] + x[3]*dt * np.sin(x[2])\n",
    "        x[2] = x[2]\n",
    "        x[3] = x[3]\n",
    "        x[4] = 0.0000001 # avoid numerical issues in Jacobians\n",
    "        dstate.append(0)\n",
    "    else: # otherwise\n",
    "        x[0] = x[0] + (x[3]/x[4]) * (np.sin(x[4]*dt+x[2]) - np.sin(x[2]))\n",
    "        x[1] = x[1] + (x[3]/x[4]) * (-np.cos(x[4]*dt+x[2])+ np.cos(x[2]))\n",
    "        x[2] = (x[2] + x[4]*dt + np.pi) % (2.0*np.pi) - np.pi\n",
    "        x[3] = x[3]\n",
    "        x[4] = x[4]\n",
    "        dstate.append(1)\n",
    "    \n",
    "    # Calculate the Jacobian of the Dynamic Matrix A\n",
    "    # see \"Calculate the Jacobian of the Dynamic Matrix with respect to the state vector\"\n",
    "    a13 = float((x[3]/x[4]) * (np.cos(x[4]*dt+x[2]) - np.cos(x[2])))\n",
    "    a14 = float((1.0/x[4]) * (np.sin(x[4]*dt+x[2]) - np.sin(x[2])))\n",
    "    a15 = float((dt*x[3]/x[4])*np.cos(x[4]*dt+x[2]) - (x[3]/x[4]**2)*(np.sin(x[4]*dt+x[2]) - np.sin(x[2])))\n",
    "    a23 = float((x[3]/x[4]) * (np.sin(x[4]*dt+x[2]) - np.sin(x[2])))\n",
    "    a24 = float((1.0/x[4]) * (-np.cos(x[4]*dt+x[2]) + np.cos(x[2])))\n",
    "    a25 = float((dt*x[3]/x[4])*np.sin(x[4]*dt+x[2]) - (x[3]/x[4]**2)*(-np.cos(x[4]*dt+x[2]) + np.cos(x[2])))\n",
    "    JA = np.matrix([[1.0, 0.0, a13, a14, a15],\n",
    "                    [0.0, 1.0, a23, a24, a25],\n",
    "                    [0.0, 0.0, 1.0, 0.0, dt],\n",
    "                    [0.0, 0.0, 0.0, 1.0, 0.0],\n",
    "                    [0.0, 0.0, 0.0, 0.0, 1.0]])\n",
    "    \n",
    "    \n",
    "    # Project the error covariance ahead\n",
    "    P = JA*P*JA.T + Q\n",
    "    \n",
    "    # Measurement Update (Correction)\n",
    "    # ===============================\n",
    "    # Measurement Function\n",
    "    hx = np.matrix([[float(x[0])],\n",
    "                    [float(x[1])],\n",
    "                    [float(x[3])],\n",
    "                    [float(x[4])]])\n",
    "\n",
    "    if GPS[filterstep]: # with 10Hz, every 5th step\n",
    "        JH = np.matrix([[1.0, 0.0, 0.0, 0.0, 0.0],\n",
    "                        [0.0, 1.0, 0.0, 0.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 1.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 0.0, 1.0]])\n",
    "    else: # every other step\n",
    "        JH = np.matrix([[0.0, 0.0, 0.0, 0.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 0.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 1.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 0.0, 1.0]])        \n",
    "    \n",
    "    S = JH*P*JH.T + R\n",
    "    K = (P*JH.T) * np.linalg.inv(S)\n",
    "\n",
    "    # Update the estimate via\n",
    "    Z = measurements[:,filterstep].reshape(JH.shape[0],1)\n",
    "    y = Z - (hx)                         # Innovation or Residual\n",
    "    x = x + (K*y)\n",
    "\n",
    "    # Update the error covariance\n",
    "    P = (I - (K*JH))*P\n",
    "\n",
    "\n",
    "    \n",
    "    # Save states for Plotting\n",
    "    savestates(x, Z, P, K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Lets take a look at the filter performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plotP():\n",
    "    fig = plt.figure(figsize=(16,9))\n",
    "    plt.semilogy(range(m),Px, label='$x$')\n",
    "    plt.step(range(m),Py, label='$y$')\n",
    "    plt.step(range(m),Pdx, label='$\\psi$')\n",
    "    plt.step(range(m),Pdy, label='$v$')\n",
    "    plt.step(range(m),Pddx, label='$\\dot \\psi$')\n",
    "\n",
    "    plt.xlabel('Filter Step')\n",
    "    plt.ylabel('')\n",
    "    plt.title('Uncertainty (Elements from Matrix $P$)')\n",
    "    plt.legend(loc='best',prop={'size':22})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Uncertainties in $P$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotP()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 6))\n",
    "im = plt.imshow(P, interpolation=\"none\", cmap=plt.get_cmap('binary'))\n",
    "plt.title('Covariance Matrix $P$ (after %i Filter Steps)' % (m))\n",
    "ylocs, ylabels = plt.yticks()\n",
    "# set the locations of the yticks\n",
    "plt.yticks(np.arange(6))\n",
    "# set the locations and labels of the yticks\n",
    "plt.yticks(np.arange(5),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "xlocs, xlabels = plt.xticks()\n",
    "# set the locations of the yticks\n",
    "plt.xticks(np.arange(6))\n",
    "# set the locations and labels of the yticks\n",
    "plt.xticks(np.arange(5),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "plt.xlim([-0.5,4.5])\n",
    "plt.ylim([4.5, -0.5])\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "divider = make_axes_locatable(plt.gca())\n",
    "cax = divider.append_axes(\"right\", \"5%\", pad=\"3%\")\n",
    "plt.colorbar(im, cax=cax)\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Kalman Gains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B+LbDcW+CNnq8DVom0T/0Yzh+jpRS26HNFX4Z2jnwS2hFxlocjmV0A7QOLCLKAqJPoSMioggQkYEAypVSL2a6LekgIuuhVen2U5mXiDJMz8QYo5Qalum2pIOIfBPAm0qpi103JqJIYKBLRERERL50t0CXiLJPKKnLInKFaAuLF4lIX4v7LxGRtSLSJiK/Nd13i4js1P+7xXD7+SKySd/nYL1gABEREREREZGjpEd0RVs2ZAe09TNLoFXjvE4ptcWwTR9o83buATBZKTVev/0z0IoPXACtUt4aAOcrpSpFZCWAv0Ob0zQV2rpm05JqLBEREREREeW8MEZ0LwJQpJTarU/mfxdasYFOSqlipdRGdK2dGfMzALP0xeErAcyCVqn1CwBOUEot09fhfBvAr0NoKxEREREREeW4MALdUxG/IHgJ4hfeDvLYU/W/g+yTiIiIiIiIurH8EPZhNXfWaz603WM971NE7gBwBwAcd9xx53/ta1/zeOj023Sg2vL2L3yqN0463vdqCURERERERN3KmjVrjiilTnbbLoxAtwTaOogxp0Fbf83rYy81PXa+fvtpXvaplHoTwJsAcMEFF6jVq1d7PHT69elbYHn7Q1edgz/90PM69ERERERERN2SiOz1sl0YqcurAJwpIl8WkV7QFhqf7PGxMwBcLiKfFpFPQ1uofIZS6iCAWhH5vl5t+WYAk0JoKxEREREREeW4pANdpVQbgLuhBa1bAbyvlCoUkf4icjUAiMiFIlIC4HcA3hCRQv2xRwE8Di1YXgWgv34bANwFYBiAIgC7ALDiMhEREREREbkKI3UZSqmp0JYAMt72sOHvVYhPRTZuNwLACIvbVwP4RhjtIyIiIiIiou4jjNRlIiIiIiIioshgoEtEREREREQ5hYEuERERERER5RQGukRERERERJRTGOgSERERERFRTmGgGwH7jzZkuglEREREREQ5g4FuBLy1bG+mm0BERERERJQzGOgSERERERFRTmGgS0RERERERDmFgS4RERERERHlFAa6RERERERElFMY6BIREREREVFOYaBLREREREREOYWBLhEREREREeUUBrpERERERESUUxjoEhERERERUU5hoEtEREREREQ5hYEuERERERER5RQGukRERERERJRTGOgSERERERFRTmGgS0RERERERDmFgS4RERERERHlFAa6RERERERElFMY6BIREREREVFOYaBLREREREREOYWBLhEREREREeUUBrpERERERESUUxjoEhERERERUU5hoEtEREREREQ5hYEuERERERER5RQGukRERERERJRTGOgSERERERFRTmGgS0RERERERDmFgS4RERERERHlFAa6RERERERElFMY6BIREREREVFOYaBLREREREREOYWBLhEREREREeUUBrpERERERESUUxjoEhERERERUU5hoEtEREREREQ5hYEuERERERER5RQGukRERERERJRTGOgSERERERFRTmGgS0RERERERDmFgW4G/CFvOs6RvZluBhERERERUU7Kz3QDupsreyzHoz3fBgD0aRqX4dYQERERERHlHo7optn3emzNdBOIiIiIiIhyGgNdIiIiIiIiyikMdCNib0V9pptARERERESUExjoRsQTBUxpJiIiIiIiCgMDXSIiIiIiIsopoQS6InKFiGwXkSIR6Wtx/zEi8p5+/woR6aPffoOIrDf81yEi5+n3zdf3Gbvvc2G0lYiIiIiIiHJb0oGuiOQBGALg5wDOBXCdiJxr2uw2AJVKqTMADALwNAAopcYqpc5TSp0H4CYAxUqp9YbH3RC7XylVnmxbiYiIiIiIKPeFMaJ7EYAipdRupVQLgHcB/Mq0za8AvKX/PR7AZSIipm2uA/BOCO0hIiIiIiKibiyMQPdUAPsN/y7Rb7PcRinVBqAawGdN2/weiYHuSD1tuZ9FYExERERERESUIIxA1yoAVX62EZHvAWhQSm023H+DUuqbAH6o/3eT5cFF7hCR1SKy+vDhw/5aTkRERERERDknjEC3BMAXDf8+DUCp3TYikg/gUwCOGu6/FqbRXKXUAf3/tQDGQUuRTqCUelMpdYFS6oKTTz45iaeRHsoy5iciIiIiIqKwhBHorgJwpoh8WUR6QQtaJ5u2mQzgFv3v3wKYq5RSACAiPQD8DtrcXui35YvISfrfPQH8AsBmEBEREREREbnIT3YHSqk2EbkbwAwAeQBGKKUKRaQ/gNVKqckAhgMYLSJF0EZyrzXs4hIAJUqp3YbbjgEwQw9y8wDMBjA02bZGmTInexMREREREVEgSQe6AKCUmgpgqum2hw1/N0EbtbV67HwA3zfdVg/g/DDaRkRERERERN1LGKnLRERERERERJHBQJeIiIiIiIhyCgNdIiIiIiIiyikMdImIiIiIiCinMNAlIiIiIiKinMJANyJmbz2U6SYQERERERHlBAa6aaYgmW4CERERERFRTmOgS0RERERERDmFgS4RERERERHlFAa6RERERERElFMY6BIREREREVFOYaBLREREREREOYWBLhEREREREeUUBrpERERERESUUxjoEhERERERUU5hoEtEREREREQ5hYEuERERERER5RQGukRERERERJRTGOgSERERERFRTmGgS0RERERERDmFgW6E7DhUm+kmEBERERERZT0GuhHS0taR6SYQERERERFlPQa6RERERERElFMY6BIREREREVFOYaBLREREREREOYWBLhEREREREeUUBrpERERERESUUxjoppmCZLoJREREREREOY2BboQolekWEBERERERZT8GukRERERERJRTGOgSERERERFRTmGgGyFtHR2ZbgIREREREVHWY6AbIQ9N3JzpJhAREREREWU9BroRUlhak+kmEBERERERZT0GukRERERERJRTGOgSERERERFRTmGgm0E/7LEx000gIiIiIiLKOQx000xBOv++IW9OBltCRERERESUmxjoEhERERERUU5hoEtEREREREQ5hYEuERERERER5RQGukRERERERJRTGOgSERERERFRTmGgS0RERERERDmFgW4GCVSmm0BElPVKqxrxp7dWob65LdNNISIioohgoEtERJFS29SKYYt2QylvnYG3jFiJ2VvLMX1zWYpbRkRERNkiP9MNICIiMvrLuHVYuOMwzvjc8bj07M+5br+zvA4AUFxRn+qmERERUZbgiG6aMVmZiMjZwh2HAQAllY2+Hvfy3KJUNIeIiIiyEANdIiKKpIcmbs50E4iIiChLMdAlIqKUenV+Ea55Y1mmm0FERETdCOfoZhCrLhNRd/DM9O0AgA37q/DtL54Y6r4bW9pD3R8RERHlBo7oZtDleWsSbvNaZZSIKNvUNrkv/9PQ4m+JoEM1TUGbQ0RERDmMgW7EfLzxYKabQEQUmpa2Dl/b7znCyslERESUvFACXRG5QkS2i0iRiPS1uP8YEXlPv3+FiPTRb+8jIo0isl7/73XDY84XkU36YwaLiITR1qjj6AQR5ZJ1+yo7/950oDqDLdF0dCjUNrVmuhlERESUYkkHuiKSB2AIgJ8DOBfAdSJyrmmz2wBUKqXOADAIwNOG+3Yppc7T/7vTcPtrAO4AcKb+3xXJtpWIiDLn6enbMt0EPDdzO7756EyMWLwn000hIiKiFApjRPciAEVKqd1KqRYA7wL4lWmbXwF4S/97PIDLnEZoReQLAE5QSi1T2qTVtwH8OoS2EhFRkiauO4CCLJ1mMXbFPgBA/4+3ZLglRERElEphBLqnAthv+HeJfpvlNkqpNgDVAD6r3/dlEVknIgtE5IeG7Utc9gkAEJE7RGS1iKw+fPhwcs+EiIgcKaXwj/fW4y/j1ma6KURERES2wgh0rUZmzaWD7bY5COB0pdR3APwLwDgROcHjPrUblXpTKXWBUuqCk08+2UezM0NZPjUiouywpKgi002IU1HfkukmEBERUQSFEeiWAPii4d+nASi120ZE8gF8CsBRpVSzUqoCAJRSawDsAnCWvv1pLvvMSVxdiIiirLW9q4ryzkO1rttvLq3xtf9Cn9vf/vZqX9tXN7IQFRERUXcQRqC7CsCZIvJlEekF4FoAk03bTAZwi/73bwHMVUopETlZL2YFEfkKtKJTu5VSBwHUisj39bm8NwOYFEJbI2/X4bpMN4GIyJP6lnbXbR73ORf2vvEbfW1/lCO6REREZCHpQFefc3s3gBkAtgJ4XylVKCL9ReRqfbPhAD4rIkXQUpRjSxBdAmCjiGyAVqTqTqXUUf2+uwAMA1AEbaR3WrJtzQbvrtrvvhERUYY0troHt5mkmBZDREREAPLD2IlSaiqAqabbHjb83QTgdxaPmwBggs0+VwP4RhjtIyKicPzv2PQWoWpqbUfvnnmet39v1X5ce9HpoRx7e1kt3l21Dw//4lx0k6XciYiIckYYqcuUhFPBStFERHYGFGz1tf2avZWhHfuWESsxckkxymqaQtsnERERpQcD3Qz7pDRmuglERIG8t2pfyo+x72hDyo9hJxbgVjWwgBUREVG2YaBLRESBvLOye9QU4JrBQEtbB/r0LcDLc3ZmuilERESeMNAlIiJyUFLpLfNmY0kVdnhYcikbxYqQvblod4ZbQkRE5A0DXSIi6hY6OlJbkfnqV5bg8kELU3qMVKtubMX0zWWZbgYREVHSGOimmQIrdxIRpYpTKDtzy6HUHdewrFE2L3H0j3fX4c4xa7Df49xopRR+PWQJnp2xLcUtIyIi8oeBboaJ42UZERGFpaW9w/O2rcZtPXxNz95a3vn37iP1fpoVKfv1NO0mj+slN7d1YP3+KgyZtyuVzSIiIvKNgS4RUTe3fHcFrhq8CM1t3oKbdErl8rXbymps75u47kDn314C5EZDYJjFA7q2mj0GvjEdHQrlXJaJiCijGlra8Pay4qzONEoGA90My0P0LiyJqHu5afgKFJbWYF9F6pfyWbarIuXH8GrVnqO297W2p/ai4IoXF+LKlxal9BhOKutb8PKcnZ7nLT/w0SYAQG1Tm6ftX1uwCxcNnOM5BZqIiMI3oGArHp5UiHnbyxPue27Gdnz7sZkZaFX6MNDNsAE9R2S6CUTUzaU6qDNaUnQkpfuv8xiIZdq2slpsOViD6gyt0fvgxE14ftYOLLXpeDhc1xz3721l/qpJL9h+GABQWsW14omIUm3etnJM23Qw4fbYOvANLYkDa6/MK0J1Y26vE89AN8O+3SNxqYbC0uoMtISIKHq2HrRPL7YyvTAzFYNb27rSm1t9zAVuylC6eH2zdtzWjvi2xi6Kbh25Ku1tIiIie02t7dhjUQNCKYVbR63CXWO55rsZA90Ien7mjkw3gYi6oRdn78x0ExIcqml238ijVNa8//cHGzr/jqX5JqO+uc1XwGynoaXNV0p6ux74Nrclf2wAaAppP0RE3d1fxq7F/31ufsJvw9aDubl+exgY6BIREQCgwCLtifxbt68q6X18/ZEZ+NNbqxNuHzKvyNc85z+OWoVLnp2XcLvdSLn4rP5lt3mHXvjkrjFrfO2PwrXzUC12Huq6CC6vacJf31mHRos0xpj65raMpdQTkb1F+tSfdlNthQ4Phabqm7NjWk/YGOjmgFfm7kSfvgXYUuovxY+I6HBteCOm2cbp0iCV1Z73VnhbfmjBjsMJtz07YzuuG7o84fZhi3ZbBsDLd1sX3CpP0/tunBdW19yG7w+cg5UORcAoXD8dtBA/HbSw899PTduGKRtKMdWhU+sHT8/Ft/vHF6hpbGn3vOQUEUXPfyYkn2mUjRjoZrnRy4rxnJ7qfOXgRajrpj02RBTMuBX70no8xbXDsdilIFeQlOUnCrZaBsB+Ha1vsbw9jMC/8EA1ymqa8NyM7cnvjELR3qHw0uydqG3qGsGttBjNPefh6bhwwOx0No2IQtDdO6gY6KaZl0u8udsSS4Bb7ksp9JtUGHfbxU/OCdAqIiJ3XpeiiWloiVbH27Ld3lN+7/8wPb3fVhlnL88tSsuxAe/zlsXnDOdUjoiTprqhFUt3dXWabCypwnf6z7TtrLAybfNBDJq9AwOnbnPd1uvSUt3BoFk7sPkAC4dS9M3xGFPkKga6WWyMxUhMbVNbt10UmohSa9TSYl8zQs9+AAAgAElEQVTbj1i8JzUNCSiZ0eu2EApDWTlSl5hCfJBL8pCFedvKUVbd1PnvP729CtcPXdE59+61+btQ2dCKyw2pym5i2QONEeuUirqX5uzEL15enOlmUDdlnqNL9hjoZrF+Ezdb3m43J4uIKBkHq/0FYH5jwzlbD/nafl4ae6qnbk7NskV/Hs1iTeTNraNW4epXuoKr2NrGbaaLXqvOEze8biCKjpa2DssMqha9iv0z090zMEjDQDdLNTusvXjH24mVOomIos5qfUAnBw2jW6nW3hHeiK4x6cZPmmkU2aU0W90em5ZjnKe941At5m7z18GRa5RS6D9lC3Ydruu8bUtpDV6YlbjUoLGIWDKpxOZL6LKa9H2WiMjZWQ9Nw9/eXWd7/4qQC/r5qeSfbRjoZqn+U7bY3lfLglREFFE7D9W5b5QmnOUBrCr2dsFkN+fWT3GxNxbuBgAYByouH7QQfxzVvTtn91Y0YMSSPbht1KrO2/7fq0sweM7OzhGc0HDutCdKKQxduBtVDdndEUXZ6+ON6Vvu78O1JWk7Vrox0I2ofRUNjvePTXOlVCLKTe1JRnv7jzp/V5nN3BKd0btHJhe6b5Rima5CPWTeLk/b2cVHw+3mYTOgsrVhfxXeWZn4G248E8zrYi7ambjUlB/lKRixbWptx22jVqHYZyZGNli55ygGTN0aWlG6KRtKWbyKPHFa+ov8Y6AbUVWN9r2IXopNGReIJyKyM3jOzqQeP2VjaUgtoZhsqlg8ZUPXRdn2Mv7uePGrIUtcA6jW9vjf+ZFLiu031jd1Om9ie1uwPT5g9ltN22hJ0RHM2VaO/h/bZ5hlq9jrP83n3PyZhWWWaaB/fWcdi1elQXNbu+PUvmwwPcl6EEXl0cmaigIGulnIS27+R+sOpKElRNSdiM8ILFMBm99lkLwIM83ZOBfTz37La4OPyqVjLcU5hrm2XlOiu4OtB2tSck4GUeFjTnhsBLIyy+eRJ2tbWY3nbe8YvQbXDV2OKRvYAZhuSimc/dB0nP3Q9Ew3Je2Mv7XG3xdioBtZpQ7LS1z75nLXx78631s6GhFR1NnNU7QLpA9EfHkex9E5By/ODj76PnDq1sCPDYLznzXr91fh5y8twusLg/0mpyq13Utm2Pr9VQDi15+ubWpN2M5vEbkomb65DE9Oc/5sBCn6xTTl9OtO3znm5zp7i/8VCFoNyyLk8kvHQDei7hyzNtNNICKKhAau8Zm0fT7nUnsVZNC+O631Huu03lTiHvhkw8tSUpnYiZTNge6dY9bgjQW7U3qMxpbsTqXNZhV1zejTtwCT1ud2luOg2YkV2t28lETHaTZhoJtmKskKHX4Kv3Sniwkiitfc1h5+xVYL/JrJvGQLFXlR7FIg0Y9cPmVKKhuww2eNjHSk+PNzmjmvzS/KdBNy1pythxynZezWO2FGL9ubriZljZLK1HR+Rg0D3QjoCe+jFRN8lAA3F7Mgou7j7Iem46KBs0Pdp9/AmRfX7qxeIr8FguZs9Z+2FhZjkJbM++1nHmSU/eDpebh80EJfj1mxW5vP3O4wj9fpjIilNwcpLJVMkJ1NRdP8cnpu1Q2J6dtOmtPQ4dgdbdhfhdveWo3HfRZDq29uw2HDetRRk+znir+78RjoRsDv8+Z53tbPHK2mLK88R0TBxFIJq3xekLkZtbTY1/ZB0qmiau2+ytTs2OKipMZiHmTSh4n4xc/EdVrxnrb2Dvz93XUoKu8+FZzvm7ARAHCkLn0X3/XN1h3sdYbbI37KpI35s/Pt/jPdH+O6T4Wnpm3jXN4kVDdq35N+p2VcNXgRLhwQbidwEB0dyrFzy0mml6XLJgx0I6CXjxFdPx6euDkl+yWiaDOua+m32msq1tv06mC19bHtgrR0pGbHjFmevrXL7ZY0yeEBtE5bDtZg0vpS/PO9DZluiiulFD7eWOrpPAza0ZCqDopHp1iPglnNhzeed1HvMLGz9WBNSjqQgtp0oBqvL9jFJYdC5HUkNMxpGMm49Ln5OOfhYBWiw/4cZuvn2gsGuhFmnnfgd0RhJ9fSIur2NvocMZhRmNwafskYvniPr+0fmVyYopb4ExtZCKKlvfulNXq5qDpYHe3K2QAwb3s57h63Di/Nsc9ciF17z93mnl4e9rVmqjuCopz+aTRlQyl+/tIi/MxnSnkqPTwpGt9d3YXbZ+vqVxbjR896z64Mw76jDWntrDXzu1xgtmKgG2FjlsdPnu+rpzd5VViaG3OeiIii7LkZ2xNu2+mzIFFYWj0EzskWrzIWOgxyrRRbtsbJkbrord16/4cbccuIlZ3/rqzXOjgOViVmIlSY0pAz0aHx0bqumh6pSHX8z4RNtvdd+dIinPng1NCP6aTdJhX0/dX7AdhnjKRc94gnQtXU2h5KZ9fsLdra3m7FWTeWVGNvREZ6KVwMdCNAbH6AdptK9u84xBFaSl5zWzuemraNS7bksHEr05dmS0BbR2IQs9yw9mg6vTCra3RxzV7rLKDymuRG4qYbUquDFECykg1zzt5ZuR8LdiR2ErRZBFczCg/5P4DDS+CpQ8G0TSaTBbYcrEl7QcwLnpgV2tzLTMSmS4qO4G/vrMvAkTOvqbU9bprNrSNX4eIn53p6rFMM+8ZCbemooGdiUXkdtqR50Cj5cy/636XpxEA3wsat6LpYdSqfTuTH8zN34PUFu3DuwzMCPX57WS3aumG6ZTaZtSXARXZAz1qMZrrxO2/Yr1yeb+SmyDBlZaYhDT3Ml+RAVfTTisNQZ1OwyWzyhtJQjhd01Ddl53uWfZAqG1pxtD78TIAgSzUGecwNw1aEdi5lk8aWdnyt33Q8Y/gtWeahozAdmbc/eWEBrhy8KPUHShE/S5LmKga6WeJf768P9LhDGSwsQ9H0pt7DGcSmkmr87MWFuHXUqhBbRKnU4fOCKx2XtlYjYE6y63I7dYwXdrsO52aGj9UI8Qer9+PyQQvS2o6NJVX4xiMzMG3TwZQdI4odhlavf9wSUvw0hqKbTI/0pF7PLvtATzGPikxdP09c797Z4bUGDwfJGOhGXmyi+tRNwQrEbD3Iebq5bOeh2sDl6YO4eohWIXLRziNpOyYl5zGb6qqU3V6YmTtLN7m5d/zGtE/d2ViiFXFbVGT/XeclWHHa5o0kOh2NkvkFSPXvRyaL7URZtsW5re0d2FtR77hNR4fCtW8uwzxD4bWi8lq8vaw4brs1eyvx2vxdKWilPa/9vZtKuoo3hr08n1GlIfPAa4aM3ZJgQcVVU8/hDiwGuhFwfg/7C5bb3loV96VBFPP+qv346aCFeGGW/9TRmMYWf719xh+LdAbYFNwGD4V/os7vqPTyPemeH5t42WrXYi/Fonwf3Tji5uGlsgu+VhUf9XQ84zG648hUs4c16r1cOIZVWXrUEq1aeV1T/IWwl/PiX++ndhmnSesPpHT/2SCseeyZ9PjHW/CjZ+ejvNZ+lLO5rQPLdx/FXWPXdN521eDFCRWm/+e1pXh6+rbQ2ubls+b1F6QsTaO4xuN4/Y1+a1lxahqT4xjopl3iF96VeSstttMs2nkkqTTRRyOy/AaF7z69CncyqchWRXS8ylSxHep+jAWWvLhvvL8K9ekU5VSyZKvSBpmX2PXgpA6dtGW7KjBvu7dO5WkBM6yCKNgYS522D5Y+WKNVV7YqlJUJxvMgE9N8N5aE0LkXIDataghnfnBSn6MUWbpL+72vNoxybiurwZE658J2zT5G9N2e9aKdh/GuodBish0IUf4uTkaQ06fkaO7WXWCgm+OisjA2pU66K1vG5OocQUo9v2lSs1NYXMvqYieKF5qBxI3oZcdzytQI8XVDl+PWkYmdylYvm5/ztzLJ9Mehi9w7MsN8a+Pn43be6msfbqmY1Um8Jodrm9GnbwHmbrP/TqhvDjeA8fryPjhxc9djQnpP+vQtwKvzi8LZmY2j9S2oaYp/T4rK61zfpyteXIQLnvBW5dophd3r2XXT8JXo+6H9klZ+JTNIoJTCsl0Vkf9e9fp9utJjNk82YqBLlITmtvasnoOUxIAuF7zPUVH83U5lk6wC3fcjVhQlxjiCkctzquyUVKav4zasgPt+hwvzKH7W3Ni12Vjd1el5fbyxFN/uPxPr9lkvfeWmsFSbQzlq6d6429N5btjxM4dSfJxgz0wPPj3Ji+8+PgsXPB4fsP7khQW46uXwqg0PnLo1tH2FpcFi6pbXNcI/WncA1w1djvFrSuw3okhgoEuUhK/1m44LnpiV6WYE9syM8ObJUG6wm4OVirmlUbWkKPm0/LCCmM0Hqt036iYe+Giz+0YhczrvnS6E0xnERqHTo9ImbdfctlgKbGHIa5Pud0i9PFyb3LrRqZCpWbsbS6pw9kPTEr7njUtb7TxUCwAoqUx8Tb2caVbnvlMG2Mo92mhiSgcNQv5Axs63oMv3+K2rkBLZP3XcEwa6RElQCqhpCrcSXhBeC0OVmebgsXpy+kRxKRErQ+ZZV8PMZOZCGBcCa/amLjXLbZ6akZ+RHAAY5iF1VUz1M93bYLcfb1TgpFanffqzfHdFXOVSX8dSCgt3HPa0nrPViE1ag1ibY3k55/ydFbnDHFxvK6tNexuGLd7T+bff7IBUnl8jFu9Bc1sHljhUEx+xZE/CbV6eQtAsiFjKt9d1q828vF4bSrK/w9DrnOT9EchuiBIGukQBrdnblXoV6wHNFGNbnDz4UfD5LVbzrqI+PyUqzuk3HWc8OI3zmnWZOG3sUszCaMssiznERwMGYU4afRZPSXe113S9r+0dCte+uRw3jVgR6PFTN5Xh5hErMXr5XveNI8IcREzysNZmFK31+FtlZ7geQKb6tycXKiWnm5/CU0ZOr7RTYbFkpxfEOkQaWtwDbPMc5hi37+SGljbcMGw5io84L80UpoFTmalnxEC3G6jwMdpA3hkrLNb7XKYnbF5/9JO5NBi/OjFQiOpcxqiJ/RiGna6XrFQEY6R5ac7O0PdpDNaj0seUiZGS2Pfd1oPBOhhjy/rsC5h2aMfLBbORn7fQ6YI/rHPhSF1z/Ciwvl8vAYXXJadio6tBm5yJLCSvr286QmOllK9pJK3tHWlbCvCxKVrdDquAN+g5+vLc1BbiAoDdh92D0MU2593QRYmj30Y/eX4BlhRV4M+j1zhulzr2Z+UBi9T0XMRAtxtIprIcRVNYVWiTndu1bl/2r9GaTkciNlcsysvwxEtvVBeltWHDeua5ME0hnRkkQUb0ag3TWMJqaew7+lCN/XdHWMcyr8MbhnS9ZVH6zHoRtL1PFGzFmQ9O8zwV5swHp+HKlxKLSgV9X5wetynD6cFVhirRfjudYoxPz+ktWu1xKkypPl2s1VT5MwqFBVfsyd1Ky0YMdImy0HrTAuNBvyadCnh48e4qjui6MWZU9P94SwZbkqjWJh3LSpgXklZFTpxEZfQyVfZWJJfW5rewiV0Q5/U9zuX3w/zdaqVzpDNFY3hbQsr8COszO3pZV4r3wp3hrNV7uMZ5zeYDVY2WUwJiHDtusvz8jDXfqmNn7ArtvfCzrOB2w9QqpxoB5Z3vSeI2O8u1aTejltqPYAY938I6T++b0NVxO9xlpNULp7TkTHQcZltnTlQw0O0G9qRxbkB3stewRnHQynsxSin06VuANxZYFwIKC78n04/pwYn+8d66DBzV+uxP9zWxVSvWBlxqJWakRfGYTHh+1o7Ajw0yWus1JXPO1kOYvrnM4piJ25bqtQjMS8VYVSMftbTY0/H9mre9vPNvu5fF73f5yMXBz5HFhsJFYS11M9glJfUXgxfh9rdXh3KsMAWZaxk0k2fEkuJAj4tp9DmlqqnVfZT4nZXp7dz28rUQ28Y4nczTV4PLNn97p+t3KunOvSzvfMlmDHTTLBPn+syQ0lwpnnF07q/vJHfhHpvr+uS0YEUEnijwNlI4Z1u5+0Y27HoTs6WaMEVHMus3k8Z44WV3UZeNIwAvznaf2+y3cvVtb63GnWMS58g5pQwOMK372dCcGDR4Gf11ks6R8bcNo7J2kmlOXMpnwBPvqw9MxQN6wcTKBu/ZJqlg9xQOOYxE2z3vDwKutTp/u7/f67X7KrFsV9fSaOPXJAalH607EKgtyXJOzw12vqT66624Irw5/EE/W8lO18jG34CwMdAlioBpFqMNfmw+kPoiRy/YjNZk+oKkuygqr0OfvgXY7VK52e1+o2z5EWxIc7G3TL4s5gvpbEwTTqYA4uytwTpmlxQdwd3j1vq6MByZ5IiZWdx5k8Y3LsxDGYO1MNIz7arVWmnvUBi3Yl+g46TyMxuFdYrNrNr0m1eX4rqhy709PgVPKUqVqoO+Z2G8Lq/N34UzH5zquHyZ1+km41YG+zxQFwa6RBHgd43SKAUo93ywIW3Hqm1qRZ++BejTt8DXBVQmhZW6fM0bywAAP35+geN276TohzHMixi/1xJ+l9WJsm1liZ1SxgudtjRVSLVi9x77nf4StBpyMm4cvgIfbzyY9IWqp1TJ5A7hS5j19P1+ZwbtdDDKxFInQd6foN+bQUf1nQo5VtTbdxTFPqOt7QpD5hWhqbUdSqm4Dp6gyx3GgsN0z7UN63omKoH209O3obVdoSWEbLeN+7sKfEXj2WUfBrrdRLrSS/ccqe8MRPr0LUh67ll3sdSQbuRFlJb1WbAjnOIkXnzz0Zmdf3/L8HeU3faWv3lmTa3tuGn4ChSVx4/M+p1vRfHsLqasRgCrGlPXiXLvB6mtdO3lIt/vheUO04Vzpse3wr7gs1ojPCrsRqi9vIfmyq5V9dHtHKwMsZZBU4COsfs/3OSrOF9M0BUQnDq07vHwHfHuyn14dsZ2vDZ/F758f1fKN4AcW6/d/dsm8Oitr22dt56+ucxlCbD4xxuXiitP42oM6VpqKkoY6HYTfir0BXXPBxvwf5+bH3fbb15dij59C3yPWHZn0zYddN3GaamJXGU1qhTlC9SYumZ/yxz8YeRKLNp5BFcOjl8SItOjmh0+h8qc3pt0LhPjxup3f/52686bbJuPnmxAmM7nWxXSFIgNJVXo07cAayyW/4hd5JnnUtZ46NhITRZNtNKbk/lcJvuZDnPd6bvGrg30OC8xgNP847BOkToPAXfs9yC2jI5lkSiHtraluVCC0+kR+HULayQ4hP3cOWYNrn5lie395qdvXPbz8RSuxmB+aq+nuOBpFDHQjYzoXPgFcemz8+J6qMzOemha1qSaZlqyBU782l6WfJrhvhCLNtgxd6IAwL1pTJtOl7V6Sls6Oof8pHoN8llR99/vr/e1faqDqlqbNULt5p5beXV+6i8SzO+I3S9DOuYNfpjGwjVWBXvMow9eYqlYhsm8bYmdFYs8LI2T1OuaodzCUOfohrCPF318pprbwunAMwagYXx3xs+3Tnp3SSvYeBCT1nd9HluT/L78zwRtBHihISMr3f2PB6tT11Ed/Lmk5kXYG/AaKdnv+TJTzYdsGBwIWyiBrohcISLbRaRIRPpa3H+MiLyn379CRProt/9URNaIyCb9/z82PGa+vs/1+n+fC6OtUfWHvBkp3X8qU1nu+WCDp+p033p0ZtJfzskqKq9Fn74FeHtZcUbbESX7klwaCQDm7whezdkLu9ECvynf3YHViIOXLAE383xWAPV7oeF3aRo/RbcAYMqGUsvb/cxDtFpGzHeapEUkEeYF5lxDZfWtB62L1NktW2I+dZoDZhGENfq5MMC0iIq6rhTY4iP1nSNeQHivs5fRy8k251t3MGGttyrDs7ccwtkPTUe/iZttt0lr9keAQ6Wiw8lqj38ZtxZ/f3d950jukHlap5vVy+PnJbOqIp2K/hqrNj2pz98OLVvC536szq1HJheG1JhggqTdO7llxMpQ95eNkg50RSQPwBAAPwdwLoDrRORc02a3AahUSp0BYBCAp/XbjwD4pVLqmwBuATDa9LgblFLn6f+l9ko6w/5Pj9R+uP75nr/RFa92HKp1HMk1O/PBaSlphxdKKfzkhYUAgIcnFaKkMvWjkGbjVuxDn74FjgWKUvWzXp3C6sgPT0rt+RtGMJ4tUjGSu6ci+mtp+80s8LIMTTpU+5zPm85Bv3k2KdgbfGaNmNtcmqZRAT+FuWLXrKOXdy2jc+lz83HbqMQ58q/Oi1/D1e98VyvmStOrihPrUxysbnSsxGrVHrutja2x3aNpP1EqYgh0dZAa3zOzYYv2YFtZDUa4rAM8OEDqc5CgIh2vYexc9rJM0/aAhaeSZTVoEXRZKSfpHmW2mxaWrnY4fbcGeXnN0xYjNGsobcIY0b0IQJFSardSqgXAuwB+ZdrmVwDe0v8eD+AyERGl1DqlVKzbsxBAbxE5JoQ2URq0dyhcPmih78f9Xq8em27vroqfw/KDp+elvQ2xghHbDCMt5pGpVI162wU7UZovaefe8fbFOfZmQRAXVct2Z2ZEPEqnnJ+2RKjZaam7YHcEL2uypktVQwuqGhI7Dl/Rg1njOR4bfasIsfBRTL3FOrtmNY1teMMwN8/TuZfmAc2FHtK77R4bYxfwBCn2tGJPBa54cVHcuvVWFhf5XwppoGmt5KA2lVSjurE1tCC4vUNhzPK9aPcwj9ZqCagOBdQ3t6W0U804xzTWeeN0PKeR72SrJVuOavt4fPLH7zqasaM6jN854z52H46/1umOhaWCCCPQPRWAMYIo0W+z3EYp1QagGsBnTdv8D4B1Siljd8pIPW25n9h8c4rIHSKyWkRWHz6cvuqvYUt1J+HO8vBTl7/6wNRAj1ux52ja56ECWlVFs0wFef87rqtYhrnCa9hrO6ZLKqsCr9yTWFgm5pnp21N23Kgwf17qHQpcWX2XhHGam7+Cvys7UNz7enxdihMqRGvb2+8rU6MQ1jJ/sRAfJJjus3nzjCnKURPW75n5ucculo0jvef1n4Xz+s8K6YjJfVa8XDBvPlDtuo1fXn7HvP7WPTYldYVx0r0ethVjIBJGZoJSwC9fWYwbh62wrQPg16YD1Xho4mYsKQrWETmgYAu+/sgMFHtYFsz4ve7n1D9iyGB4fWGw2gXJfvOmezmhqoYWy0xAu2kK60JedcQ8WOOl5gCFE+haXlf52UZEvg4tnfnPhvtv0FOaf6j/d5PVwZVSbyqlLlBKXXDyySf7angmqBxZCctuvptXvx5iX50uFex+5DOVEhtWhdFUGDKvCOf0m+4pxc7ouZmZCTgLQph/mk5BRqDNP65+pguExfzN9Y/8CQCAq/OWuI62xMzsdS/+kjfR0/5zVSrS+8Ji17KotDn2NT7ElHqcbtss0uy9zNVMRwEx+2N7E/Sd3mXo7PK6Dy+BSvLrIsfvYOii3TZbxr8/fgPATQeqscLUIVtUXodnpsevI3ygqhGFpeF3dhiVVmvzboe5pHvb8ftxd1oTOMapYKXf45XXNsUF2oA2dcDvuev33Lr0ufmWmYDFR6yf26NT4qd0KaXwzPRtoU2bi1JmVJSFEeiWAPii4d+nATBHQZ3biEg+gE8BOKr/+zQAHwG4WSnV2S2klDqg/78WwDhoKdIUAXXNbfjrO+uS3s+TIaUNeTHLZq27dI4GOo3C+RE0XcXuR6C+Jb5dz87YjsbWdl9FegBgeMAfVTdLd/lPSYsyY+qimyCViP3sPxmX5GkZEn/OL/C0/WU91uCsHgdwb8/3Qzm+28VRurI1/F5cRSNktLbOlDmQypcwmbS7/QEvFKsbW/HRusROIi8Bl/F8S9V3Xeex4B5keel88Nuplsz77TS9RCnlWmHX+HQ+3mjdkR5krV1zNkzQUdcZhWWYWVjm6/N78/AVCZXa//upubhq8OK42zaVVKdkpN/ow7UluHuct2WXxizfF+gYTqdkLPj2y+qUvGjAHFzwxOzOf6/YcxTnPzE7oaPBbPOB6sBVh0cv29s5QGG+ljN2kOw+Yp89ufVgLV6dvwt/cVj+KvWdivYf8lTWccmkMALdVQDOFJEvi0gvANcCmGzaZjK0YlMA8FsAc5VSSkROBFAA4H6lVOcQn4jki8hJ+t89AfwCgH1ZPkqbtvYOfOORcCpEv7Fwd9qqMP/tXevAPFWjgT99YQH69C2IGxW1C3T990KGe/X5z/esl+ixK2TjJBVzRgo2ur9HqUybzqSJ69NXufWTaEC//NHoBesfO7+/v+bg4Rzxf/FU3Pt6FPe+HoD/yvFztnpL7fU1RzdFgV+QuYup8GaKOkmsvrMmeli6KOyX+5/vbbD9vksbY4EoiydoXm7HU1qyze2xyrzpZv6ueGbGdlz85FzPqZx2I4SxGhd+OK//7f1L7c+j1+CO0WsSbndKx271+Hv4y1cW4xcvL3bfMAn/en8DPjb9lpbXNvnO3IqJ64zpLJ7lbx+xczsVoV17RweKyuMzL37x8mL891NzO//d1qHw/qr9CZkWlzwzD6+ZOihWFncF0U4dXdM3l3X+bT71Ysdp8VFfwWnLsDNE6lrCGYyJmqQDXX3O7d0AZgDYCuB9pVShiPQXkav1zYYD+KyIFAH4F4DYEkR3AzgDQD/TMkLHAJghIhsBrAdwAMDQZNsaZT/NS/wCDdvOJOfFtXconBFy1eSbhq8IdX92mlrTt6yRUqpzTvTUze5BmltRmX0VDRnpaXtnZYDAJAWFocaucG9HJipoB+V03WUuTBYb0TUHjcaL38O1zY5VvL36V/4HuC1/Gm7P8zZCa9ceLz6PxJ53p4uk49HgqSpzD3RAoL1mlRYFiqxEIfvLOC/S/F6bL06zgum9tFojt6G13XfH2N6KBnyn/0yUVKZw/c0kHuv3Qv8ji2B/xOJi38cNdR3dEKIO8zkcCxp2HIr/fvN7rEZTleRDNU14b9U+Xx1FTsGB8XWs8VhNfdTSYs/HjpqLBszBAENmXTqyYJK9DvVqyLxdnats2Bm1tBj3TdiIZ2fEZ/btO9qAp00p50bml8kuI8T8aobxO03+5YexE6XUVABTTbc9bPi7CcDvLB73BOPxxKoAACAASURBVIAnbHZ7fhhtoy77jjbgzM9/MtBjOzpU4OJTTpbvPor2DoW8HtFN6Kusb8FDkzZj4P/7Jj51bE/X7Vfv7eq1Ns7FtfsJuecD5xGGS57V5oQUP3WV43b1zW047hj7j/ScNBSv+ckLC7DnSed2psLyPUcDn9tR4mWuk9mFA7QULrfzw80XRAs+tdTixH5Fv4U/dpp60y/s0XUxcYK4d0zkod3wt8VSFhaP2d37RgBAn6axHlvpj2Vams+vrlQUBkyVsEZcDlZZpy2+ONvf2smxtEOn4nRBhBHceYkRzNu0WGQ0Nba24xO98pJvkMWxIzLVutMHq7s6QOyKMDq9rJPWH8DAqdtSsp76n95OXJbKr2yYQ+l3ebRkPfhRYnJm0HTdsM7n+QGy14L4w8hVAJw7UVKeuOxwTmbDChxBhJG6TFkiaFbpkbpmfCUFQW7Mn0cn/4PiZFOJ89yXedudA8DvPD4LBRsP4jHDQuJlFvNNYqnJzYbR44cmdn2p260JaVUQKzbSYbXOn92PwrBFXek0Ta3tCY/1ss6g3bqC0zymeIf9Pen1i7ffxGjObFgSYMkLo9izt6uwa0yHd0pBq/Ew4nFF3irH+8tru875fLinOB2pi++9/lGe/Rw+AAnzq36bF98bbz4VnObanSZHPI/MmUcVnUYZg3REkL1MVN83e2n2Tqx3eV+Dpnda8T1VxfD3iajFt2QXlFIQAQQdeCT/LZymutIlz5G9+HWPoGmwKkALgzOP0MbYtcD8HdDYov3WOmd72D8f809pa0cHXp6zM2EqjPG4foKrhixLBa1saPFcKCv+dYh1iAU7d5zmzUYx+PKaMmxue+y3xc88YefA1PNuEgSp/5GtGOh2I/eN9z836enp2+Im/afC7K3lKV0P7CWXAO+AxzS4Dw1pZsMXJ85liy16X9dsHVT0neB8oW8UCzjN80ScGOcifa3fdHyt33TPj4351/vrLW/3s26mW8eCH2M8pC1HmTkVWeP/XJ+wNj7181E91dW4duTWshrYeWOBv7mXVoVRKg3ZCfkIf060OXA9Fl1VNT8niYGInwwFp9TGZEc0Fu6w78wIcpGWiVG3VM9xt7ow9PI003GNO2j2Dtzn8t18pL7Z8f6YMN+6PLTjyfyhUEe7OjDH93oMk4/p1/m9eI7sw635M/BMx3Od20w75n682OvVwMcN5fxL0zkcO6+sqmA7PMjWB6tL8PysHRg8171T2IsoLKfkx/+8tiyhUNb2slqsKvaWQeF3FYvohbDhStfz21jiv8MwldM/ooaBbjdS6WOeZ3NbO/r0LfAVaCXjH+9ZB1hhcKse/FDIo4F3jrGuqGe1sLsb4wjrDpe5LbEvVeN8VbfRarNlIaSA/c/rS5PeR8z7pnXjcs36/VWYtL6rAyVxDdU0N0jnlk5v5redfp/WkJ4vuW7zdSm2PUIqLzgfmWT//RFmBexUphgaz0EryRYiC2pvRT32p3kJuKAjSF4e5bTr2/IK0Df/nc5X7luyG9flz8NLPYd0bnNGD6043T5TLQSBfbsFHXg4/218RUpTXtH1R8/Ow89fWpT0u298Jukc0WvWf2+dOn4+XOteRC2X/OzFhfjd68vibuvTtwDLd8dfKxytb4n7jvrta0tx4zCLGiwuJ0dzW3vcWsexfXcXQT6iYU3liODgeSgY6FKC9g6Fsx/yPxqYjGTX5Y2CML8krHb1RIFWNMKtyM5BQ1p1ui8SAST8SCVjU4qXXMiEtXu7el9/PWQJ/v6ufSfP1oP2o7RmmfyRWuwzRftzUuUrrfsEaXBNF/sEus77P+VNdY08PoU6HIPEz1KYF9aTA1TN3muz3qS5Gm8QdT6XOHN7JYKMJPjx5LRt+OEzietWptKUDQdxu3l+ZsifrdOkHMW9r8c3pasjpF/PsbgzfwqAIEtWKduq5H3kEP6YPx1Dez7feVvhMbfi3/ldy3wJOnAC6gMd22hvRYOv7yw7dlVtzW+D06jUszO24cv3FwT6PDs9xktxxO6gsLTrfZ69tRzffXxW3P2r91a6/i40t7YnvNdnPzQdZz3UVfR03vbD+O7js7Bid/jzsINyOqXqjJlJKf5NDvJTlavBrBMGupQgFUWnvPA7+uhF2D2BserHYfSML9rpoQCCxWHudRltO1zblWb3XpIjop3FaHw+XXNvbxB+58VFYb5fAosXbrvFyPyWUuuLw1g6vB3jnFHj++5FMtMFVJJjNoN6vuq6XNB/SVdRN4HCIocUYbM/5M9EP4eRVgDY0PsOjOs1wPM+0+WPo5znSifDLisk6JlQUeexsrXFAaJWGClm9tZDluuuJ9dehZ76vHYRwaU9tO/wa/Lmuz5SLN4dpfRRXMPn0Dzlzpyub9zPcdKMv+ZP7Pz3PfnvY2Pv2yFNVSl9X16Y5a/4mJvxFtW8Y4bM2wWlEn+LFIBrXl+GSesPJH6LRfWkzBF9+hbEjT7O3lqOxz/e4vCILlad3pl6t5yWxRxmCNz9fK/WNbdZZu04L48VXHc61RnoppnTKWs1upBur84vytixbx0Z/gWe17kldtaa1v0rqQpvhHTFbve2TTD8kMeCTtsURv3+/zUsRl5oE0DZMQfwThcSTq59c3mgxxn5Xdh9QxQDXY9ioyAzC53T7M1eX9A1teBWnwGS8YJDTFWNU53i2NvDd11sdAvQLmjeW+3caWMOCJoNmQV2z+b8Holz8bxcVjS3tXde7Hh5reZvL/e8ZrhVRsTh2ua4Incxmw9Uo/hI+Et6JcvqJfHy+ezT1//SVukwvbDMdqQ9xjgK+Ne8D3Fxj67ihXfmTcHO3jejR5P7d37stXPrTLIrbhgzaFb8uW0VMMdc1UNLMc1r8rbOrZswvz6OJrm0nlWcsLL4qHUmjU1QEfZ6pZTdXjVN6Ru9vDjpfX63/yx8+7GZCbdbLUFG/jDQjZAzJPUntNPFVnuHwjPTt9venw52I1tB3T3Oer6smV2qkp/UwzB/DGMV8YyVa2scqswCQFUK5vBtSCI1MdmqfuN8ruM7c0uZ+0Zp9twM+8/TsEWJ8zenFyb3HIK+5p+Bv7UNT7YoDuVHKsLoXpL4+SivabJ8TYyB/TFowZG6ZoxevtfTKHdsasf3Bs7R92VPROss+sPIVfjzaOe10pcUHbGtbn7hgNmWnUe/eHkxLn1ufsLtK/cctZy24Hsutb69XXVcP8xLeNQ3t4U6zSGVHp5UiBus5hsC+KocwCP5bwFKdXY8/bvneLxjyBb4e/6HAID8hvisJafg041x7WVAJQSXLe3ae/bnPK3D6Fhp8fW5e6fnE7g5b0bcbdfnzcEpOIJxK/Zhzd5wgmI3xg6SMDKFnKzTj8WwNnqinHJrXmEgZo+PTsjYUmMbfRTzDPKSLPSSSZhjGOh2M0MtLq5jEuYlZcCVgxeFur/Wdm9fBW4pojGxL9swLtSdAuOBhkXcY2I/+HYjSH4qI3vV+XwDPOFRS4uTOrbfQmhLiqIzhyfGqYCQcTmosNaBbDYFDk/nv4lFvf7e+e+j9S2hFK54sWfwqq4xfk6pWECwrawmbpkjozG9nky47aKBc3DGg9MSbv9jXtdtvdGCC56YjX4TN+ORyZtdg92Jeg/70fqWuOWd7MQKe9ktERVzw7AVuGvsWrR1WAd/fjIcrnljmeXc1g6lLJc8cbuIvHvcOus7PL6JVrsfOHVbwpJS2eSxKdqI7ciez+DW/BnIr9mH91dbZ8AcK10Xwq4vmeqwXLrF6hanUd/Ye3pt/nwAwAlwz0Zqbe/oLCR2cd4W9O/5Vud9J6AeA3sOx9heA/DAR5vwP6/ZFx1sssg+CEMYHSPGTm3z71qQgpGUOSlcrCNj9lemtq5KrIZLWAUDswED3QhJpnfXq0qHOatuF2LpEkbRFb/WeuydXqenMrdYjBK12wTVQUbZVhWnp7c8JnZxH+ZXX6x4FrkzLx8Ult/nz8cXexzGd0RLY7zkmXm45g2tgqZT54XbeXAc4oOukxE/wlvqEpQpAPsrG/HB6v2oNFXrtHKyaL3cV7y4CBcNmON7OZy1pnVS+4h1iviY5fvwRIHznDFjKllFXUvow9OpvHi7YegKnPvwDPcNdUFrHJgL9tQ0tnpayzkb3Jg3C1+XYoxcUhx3+7xt5ahubE2YBmBkzhy6MX9OwjZnVszXtjXcttOl4r4XTtcXsXuen5k4h3b65oNxj/+0OM+tjzqvnd8ULVYdfdcNTX6KVC4Ia6pRlEfNk8FAt5sZahhFMnpvVTiVBI9DIwb3fBnrj7kdffPHxVVC9eq6EOZ3ArAd+bFiVXwESFyaqN8kvRffdJEDAM/bFNp4y2ak1Xkh8PR+44xcsgdKKVSYLmx3H05u/p9bwSE7UVnMXCmFNXsrMWvLIcdOoqj7fg+t0yFWddf8eTePDNmNKnaJ3/7JnkMBaBcjHR3Kdd6oAHhz4W7cO34jvvP4LOyt8Hee+anYDAB3jrFPGzYHAFM2JKYP/6zHKnxJtLRyYyXRI/XNCev/GpnnyGc62LPqoAO0+c9W82Nfmedcs8Hu8spcRX/U0mLbSrrZ5omeI1FwzAMJtw9foj2/Ww3pvv/91Ny4bUoqG1HV2Oo4EpvXkVhUrt7UsWP+eRC497eIRXqz2fayGlQ3tuIHPTZ13mZeLi8dHfJh2mBKBfWSaTQhYG0KSp1kM8Syxdjlidfi7R0qrdeEj04uTGlRxHTLz3QDKBr+M2GT+0aOFD7s9Qi+26PrwujO/I9xZ/7HAIC7Wv6OaR3f87Sntfuq0NGh0KNHcr1Ubyzwvoal+UIiJoxFtetsLoSd0lUV7KsOp2LE+0BVI2ZaBPvL9DlRQdNcLnt+AYqfusr34z7eaD1X0Y1SKrTezWteX4aVFsXMvnzScZjy1x/g+GOS+/osq/HfCeTG+NTzYR+ATd5Qir/++Ezb+3cc0jooZhaW4Udnn5xwDpsv1HuiHQOnbsWbC3fj1v/ug5+e83lf7fb7G+63yFrC8Xyez2/0GgQA6NM0Lu7229/yN91j5OJiX9uni1OhKLsCUcMW7UbPvMS+8qgWlAqDVcHI03vEz3k7Q7qCJPMo1NPTtmKbOh035HXd9tS0bXHbHK1vjqugfraUwDzT//TadQBO7Aw5PyO1MM/ss/pIlVZZf+cYPw+Nre34ef7KhG3O7aF12OYj/vfn4h6FeK7n6/hJ87NoRG/L/WeSubPayO53za1jgaLreDTgofwxeLztJtTj2Ew3xzerkeuvPjAVV3/7lITb9xypA+DvtzbG6jKpXp/WkmudChzRjZDjJfwLXyvmAMpvGmAiheLeN8QFuWav9XoJxb2vxyP5bzmmdsV4nTPrJBMjCFbBqd1cXLflcLbYrEe4+UD87cehESclXOb4oxRQVp2a8y9IT+SjUwrdN7KQ7HJKgNbePn0LLINcQCsw8Y1HZkS+1z8PxqrD8e9BbHkSJ/8ZvxF3jF6DBz7c7Lp2s0DhzYVax9LIJcWuOz9B4uchLfNZZGbQ7PCWKTG/NuY06p4OHQbmDIju5ImCrXhkcrDPaba4NW8abszrWh/03vz3Qj+GsXI6AKzacxRDF+3pDD6PlRa8Y0oFP7taq2WRr3/GPydVaDD9jo8zPUaAhHm17+uVzL/Sw70I3vM9XwOgLU1kdH/+OJwqFThD/K8ZnWkHq5PvyKZouT1/Kq7Nn4/b8hJrM2SzyRsSP18Dp2qdZGHNnrlt1Crf2VXZgIFuhLzW88W0HKfeVIzkrrHO1UDdFPe+wfO2t+bPwJ7eN+L+/LGO22XiAqo6yWUMAGDsCn8Bul0xG7vY0Lw2IgAU9r4Nq3vfZXuM6ZvLklozNVnPOlQetlMV8L1YsCP5ioLn9Z/lvhGAf3+wAdcHnCNUUedvzVuv7EYovijlces2m7MJzGfHr3ss7lzOZ8LakoTzMeyzyW2Obib9M3+8r+3z0Ybf9FgIq1ep3TUlnKLkkZ6j8UTPkZ1z0j8r9pkEXR0m7pedfjMKzEtrbdI7Owf0HNZ526+GLHE9qtl94zfG/TuZtOQoLI/olzmt2c7NIxJHuHPdyajCd8WtQ1Hh3/nv4zTxV99F0IHb8goCTW1z172G3/v0LUBFfeL1xJB5RXhymnWdlKtfWWyZUVZa3YQfPTs/7CZmHAPdCElXkYfpm+N7b83LPvihXdD59+f8AhT3vh6fgf2FQzJLCTQFWA7D6svCr4MWo6JVDa228zvtKiXvs1gaBLCfS9z5OIu1HjeWVOFdD3OwzR0gRslkA5vXnEulaZuTW55n6MLdvoKupbsqMCZA9oHbGphhMF5MX5c/L2Hd5kOG9MgLe8R3Rlye55yS63ahbk75v0icC5P5rbCdrO8Y1s+9Ks962ZiYL4i/76G/5E3CC71ex8D84Qn3DZ4bn/XyJSnDtyS9z52sfRo16Jv/DvKQ+NvxCYTTMeUlkIxt8xWxn74R2+a8Ht6n5/gJYq22tPvM99IzHl7qNcTz/in6Pj7mAXx4zKNxt31dinGsITj9qpTir/kTMbTnC523/W/eJLzS86W4x43o+QzeMGxzeY816NdzLO7PH2e4bRVuyfNeKM+N8XzPR5vjVJ5sN2Re4m/IszO2207f21hSjd+9vizVzYoMBrrd0L2GXtxlSSxrIujAC71eT6ota3vfifPFesTPat1Ir4KM7JmDy7AKx4xaWmw75yH5tXe7Hv892YpJ6xPXYlaAZcEcQQe+qq/dPGppse0aykqpwCOsMTt8VA3dfCC5NOygmlrbMcBiWSc3D03cbLlkixM/2dx+9v3/2Tvv8Ciqto3/pmxJb6RCQhMRkSJFAQFB7GLvCHYRK2J7EUSxoNgrWD8BG/YuFlB5FRuKUhRsSAg9hEAgpO/O98e22dmZ3ZndTQi8e1+Xkp05bWbOnDlPu59wygotrp0bSBkzzRYslGk3xVq5/CCxNGzbWkvRtd48oq0FPVTjv1Z+N65tt/EyRI+Sv4xQEv7ruJ4PHFMYKOp5sCicJH4X1nU6gfhhqu1FxskfcqSol3td8f7fWMEjmChjBY/aY0vhdYq4KOh3rKMyui5fzG5bHYVQsbCFVBNpjRJofcjX5EpPoZaPHZN4wvZESFn1GnWz7XVGapSHR0hLOUalPPVZ/9UhLM/aH+EOVTorgMPFZVGsf6Hz9GfHFSx1jLXYTgL7ChKCbgsjXh/BeCEWevY5tvviMoa3HXcYbPQCLLFWEY5l1QgXzgpmmWuKIg1BrDxImezya0zNCJdXSB/6/x4oWXP3ft1+F184buJAoTRsuQWrylkRo/B59CPmLf9T3v8tpr703LvN4IApn0bd5/AHF1oq32iBVfoRAzZvvRQ+E98OCJipmhRAsczNPzaHJ3/a25hY1Yg0dvWabaSUU2OMvMDyGHys2GoMF5fyuP1JbpDftNxeAtbh21CP9RIoNifCzTgzr6mZ9+10ybq3VTTCeriy3zgm8I79dsvjSKB5UcC2IMusGdjxfFf7qLxhYl33w9XvJ/zBHPt9UcfFC0Kg7Uxhd4tx4CTQ+pAQdFsSYg31YsvniNXD+u01hvGhZiDgZqgUK1NzAHPt03QFrn53m4uX1CIeLInuKBqxmhVHG1O51Hk5q5wXI+HiwlmhcUFaYWWYtFTVlj6eWrha99whXnfVe1RxXnpYGSPDrQ9qJtFw+LUsPElXJKyrtE4wEq1w7MOWnfWWSLf0LO9GMGIEP23mdyHH1qpc1++zPRt07okvw6WKCb+xjZRmam8WdCPFdKlzBJ8tLWzmsQSQgeee5wv6pGgJxIYDhVLaEur501e1kffB93bEOsv35HsSa9+Kor9GRGp1f9H8WpdAy+AH5zW8ar/H/zuZOkqdo7jYBIFTtk6InXUDTuTy2YLHC2yEroeFMWKc5TG3kEDrQ0LQbUGkdb2TOZ2WRy7YAhh831d0vz36eIj75OfiOBoP5jkmkaWJ2a1rdFt2CzUrUEXCwwaWtHDj0TJo+mBkTTMSph006sZxlm0LL8StMrC8hYs7tRLnFQv6T4ts6bL6rPVw/2d/RC6kQd+7rFvhtNCmRNJjSfShUcdbwIiFu7FJX3uiRybxd3lgE6LnSmgE7UZFO10f+yJ0828F8fBk2VMukEOkgIdBcwkq4S18oWcHir/rxpImYB7zHJP41jne/zt2IdZ8O7G+D2Zqh77TVmJ0Q3swqt3avNQS0ELRXSsOVmXJ8ClFwxHv6T3nWJ+8mTnZWQx8V++Xn+FL+/Um2w6Ptmwlk+CQqlLneUyTXzDVfgJ7DxKCbgvDQCm61+Es+b/N0u6vznFoP6l6lqtweCtOaV+MiJ+2VVtnlzTKpetLyQL442XB+AMQLq3KfsJ65q3QJ2PSEkLlY95KtLYyfnTzRrmBfYiFgMwHq+RqtQ0uGqya4nVwjSrmFeCTFcZkMn9uDo1ZfsmAmOzNKOezlY17rJv8wRbd5qPBENGqB4m5q2qtS7LR6A8RVjHXPo3x8tstOp59FaKJdHcBxEdA1YYVqC3LapfL1gQjgdYXk24FBWzjMMvvcwLRYIL8FqudY3CGIVQ7TvR4j6UJ0aVb0puxvv1Mjk7qw2iVQWfJ/w1KheWggTm26UF7JyPPAy2+dY7nO8e1IcfPk78IOdZR2ORnXk9g70NC0E3AMoaIzWuV/t1xcdDvPzbvspQe575PrVv0fPinPCCAGFmG3/nFuivW8wY5fdWWVjUJymXyx6ba7a+KGTxBMp8CwcrmJJrrNcL415eGPX/x7PBsv82Bm9+Ofj6nUEupcxSlzlGWSDM+/T1UIfHd6oqox6GHWOLs4m25jIfVx+qYzLq8WWk31hh8NdQW6nCbM+2ZXO+7e7QYW1q4BDzwpTgxQzQVDlbiW2+1BafXk4Xmt84HRmV8LTlhsiAY1hHMEw368KljIq/Y77VcLwHrGCV5SPHSwghqZuasXpnuYfg9fPuZA0XjrAR6vRZiXtl9iPgHh0vLuV1+Uees8Tw/XFwGQLJgzvvvK8cNzLXfbXpcCbQuJATdBCzjJfv0Zm0/RagPyd923vPRk2ZZwY9rWkc83EmiOSu22Eq1/0b4cNlGw1hWKzGukWDFff3DMC7G4XCgUMrvzkv8v/92nk+Dys3YqlCkl5oqFuxdMyMYBwuxuUqD+XRteypuMlelbJpgC7XO+kiojhP10x8dIK7TPZ5AKBw0cLT4k+651mbRtyJUW4HvWzFaMg7TeN7+EGAtvVA0yBTi5yWUgFkYz5loV0AfM7i+W3O4/ozn0lFedmYr803dV7h6vhRDBwrmUwIKXo+PnqK+sSKB1o+EoJuAJRRY0LbFAk/+tsDi9cO/laaYaj9ebuwuagaT3/XE4/1T3jI5jdVQfxaae/Mt78H4PqN4z7mL47dxN4qV1mLJ2ugUG8PEpcxzTAo5PnNhIO7JyI28pdCaLLoOQS9G3LiPK+QPQo5ZpzuJv+typDbj6d5WInrcWe0aa1+4EXznuJqnbI/EbQz7CibLr/Cs/RFODKtAjE/qoPBlDJR8LShuH6xDtuWDOt2LFlZXhGIhfM73BJoLwYRKsc/ZQKvRjSa0badgHP5l5U3Qa/sQHQZ7Hw4Nc84IV0ih3yIfugtruEF+w3KbCbQsEoJuApbwWgu6bzyrSjAOcIyJFDVXvWqNoc8In/4WLDDLNFHqHMVY6UNWbIiNFdgI3VUuPs297ZmuYVpOp+UE+0cX/K1rvZ30bvxitv7PwFVci9Ofsp40/QChjNn2+3XPffFF/BLeG2Gi/Coj4uy2qn0a/cTIaXSs4FAxNJygr2Acb24d0QvmVoT606Vvwp5PDhMHp0W8VVndhVKKhEqOk0Itlw4a/OlB/hdRLJQD8IT9yZBz8VLqtKRngJm+YhGe9cmorLV3jfReVD2Plub73ckTsI5S53ncK4dmUgj/9GLbcVglqnrQ9oy/pnGb5qF+H3x8ET7Wer0xWWk7XM749+1TuEZ+z2/1TaB1IiHotjq0bofDDmLLaWmPlpYEsQX+W7GbdZXGGudYU8So8eDnwZvwdoLHujLJNpcFq8pNtXGC+ANf2SeYXgRPlgLWhubeNHUTy4J+Xye/06z9aTHyiUVBvzfo5ISNFZFcoesarVu1Rdx86phoeP5UaZHhuXiggG2Mkz/i/7wuhuFgxdVKu1HRSyGhLh0PpITJa2jVFe4C6XPT/WpJWfY2N0ojgaOtYEzC9qfzQn5xXN5cQ9onYCa3bbglxW4iRt9oDrfsVz86oaYlrM5DxBXcbZtlEHOZgFmcK3/l/9sc8ZMZRDtvjLGfEBo2ZI0d3BgXyua/CZFb04fordPawh8SCEZC0G1lGCm2TCxqNOglhMvD2Tx42z416PeQ+7/SLwgMf3BhXPqc/W2ogPCYbYbldmbYH6ejuMVPBmEF7UVzwnS80NJxir9v3MnvGwMxiodNt36PImHSu7+FPX/iE9aF0n+do8Oev1j+lLpGV1zjjdXQc+mNB6xsYg8Roid7ay70FENd1Y3mdIoFa5E1Rt7Q+9hTMHahb+m0LKlhFAv7GjoLG4LyH0fjTmwV023PRWytjRCfnORm4Ltmt4borKXmXTT9pHkJ2rKjILhKIDyijZk1Q7Ko17KZPUU4ngFrMbpmyxmHIFwizTMYhzF85/buPPL7PhKCbitDgdA6yJD08L7jthbvs7e4OsTlbuQToe6DG3bUUhFF2h89TP1wZcixbKL/8LbRoddPAE54fBHXvfYrHSaaY5i2irmLy4LIodSoqK4PyjlrBuoUIOGwZWed5bb3NNoJ5hmfk+MkMIUXPaxZdJsLPpfXaDFW/sjwXLQCh1G9RD7TAL5w3MRPzishzMZWCzOsy+HK9DKRj/wF2wMR+44/43k0dWJ3XY4GM+2PN3sf/2uIde5f7yXFC4e2Yb4fVudNrPmezdULxQCD+N1wfUhegrdzozBmJNBySAi6CbR6/J9mc/Dbhp3c8MYy/2+3W2kWi6AaVjcLapdrSL00bwAAIABJREFUdWL2eEIvP93ehveWRsd4bBb73/pJiItyo8tNv7uNmUeN8K1zvKlyV7/6K9X15lMNWUHrEGZawxi0MB8j1pz3ULtOtKRwHs11PWR7ivfsU5phNK0DnXVcI7WI92zQPod0VaygMUt+/OekUU/N5Gxiun81ZENrYWjtU8RFHN+KPd5aAxyEKvsLhO1AJIuuMXxCbLj1JV03/25sEy1a1uV4lLMKbchSKjX+tIMJ7HnIe3oACQSjtbpAhEs23twYIv0GjQrqzcDbv6zn7V/Wc0BBGn9sbn43Jx8DKkAP4V9WKJ1M1x0mLaM5eGAcuo0G36cE4IApnwJw4aAOfLe6gr+2WLe2HmmB/Gn1hi1c82p8vAu0sB531HphdeN1tPQzH7oHGZS3wjBtHlaFx+YaRzzq6SESudbeDl8MXfj42+a1IB0mhg+h0MJMD1bGLMUpBd1+YvyVktfJb/Ng09mmyvpS2XSoGxD3ceyteN1+JwcJa+he/wIgcIv8alTtxE/5FzrXrFt0rfTmKT1IWmlpj6U/JqOeI49I+z6aUbAl0HJIWHQTMIW75Fl7tP8LJX0225YQcrXob4KRdk9Z3qJVlLRWBUs8Mfu70qiEXAjklzQDmaZmIddqLdhTc/tEyZo1J7MFmcR90MazhbtT0YZDjJff1T2+77/B1hEvpYaV9dGpY1WL2HczPLw97eJupp8OBimI/he+R/HAoeIfpAj1pHrTmvmst3qwGioSqBdZWeSDeu7rtWgljMvqDOgirI9Yprki9cXEfG3VSAi6rQyTbdFp5JobZ8qRU/s0J6baWg8Lo5mPcCrG7NAtOQ6z2JcshfFGB8FabubWa09v3o+x1fsUbjx6KXLCtqTT1C22uQa9Np/V9W5bsEIw3Dv6nP1hw3PhcKAqDZkZZKoE6o6Wn1HrRz6VzLXdTUYYxUY0QqxeTH5/nTRZWpwjLzTdd7RobkHwIjn2NGmxjFB7x9TEYno9peukktnXMEBcSalzFF2FspBzVtLmHC4uCzlmhnG8oxB7Xngz34ho57bRHFC7x1vJh+2DudEoml/G7adQy2hpvumWE4gdCUE3gYgwjqNpWaS1gPBoDpEXKG2e2uaAoLOWtl4ha+/FQscNlspfHoaAKJ4YJi5tkX60MJr9OVhjlN0b5qpVIaWPaD43cJ4Q33zcRmPtreIIyDKwImvZpfsKf1LqHLVXCMYP2p5moLSSZc6xIeesPD91ejeADmJgY+9bazuJ1jf75tK7xJ+Mak9bdM1B/1r7at6j7mFSpV0qzWO58zKKME+otzfiWHExoE+aZGbOdPMqyE7RTYEXeU48Y0IxF2kem5t58RUAe5vMFhLLe6GtGa6t2+SXuNs2iyHiiqj7S8AaEoJuAhExVFy+p4cAwEf2SXt6CIC5xTo/jAtRc6KlXL6esz1IqXOULvnF/zr0NxLxx1nSQkvlTxK/j0u/VjcE7Qzyu2rZ1GNBkmB+Hsa60c8KI9Br225NIkU4jNTMjVOkbwEYvBdsxmyCmXzY4Sy60WOg+HsMtfVxpfR+xDKxjLnl7EjWYxt9sJLb+ijJw59QHCaP9N6IgeLvdBY2+H+b+baHW9tm2IwZrcO3bN0KajQmK/uTboJx6qHo2zZ2w45FMWQlDd0Z0n8BSN6DvDf/a0gIuglExAv2B/f0EICWzy3rg2AxlybER2teYhC/5EMB22LuIxoMFZdxlPQLAH86L4yrwNLacLRozYUWrG9Cf3RcGRU7o1WlxtmScQ5qKzCy3BqNx8jSMtP2WFzGAzDSQvyutbsW+jTDbVC0bRdZSNsUHRTVX4GxdgoiQ4k8I5OF4GsaI1tnJd9TUBTj6/Pdk2iEOyHob/1vQKGpNTj8/U+mLujZqYkPrSJb9W6aueYeQuSUSM2J+GdK3Xcw1z6NLxw3+X+Hd082T7pmNXVUNO6+6nrBx8zU8+A/ttcs9qcPa8RToRgh/mK573DPIUAOl3BdbikkBN0E9io0hwY9EoZrXES7mYiRi8cSFolc4S3HnSHHZCJbN6zHUgZjrBTsmvuX84IWiUneE3jW/ojlOlZJkPJNurBqtcbhPtXdhNA5Gi9rv4/9NLR9fRhtkOLFBmsVVlyG91NZVHzQCxnwQdF8UnuGcbmMBzqp3mX13UxSeVqYsXpYOS7hikr5Fy90FDYFWdWjmUWOINKcyNc+QX5bt4y5HKXhkUL8iOum2uZELKMe84VxiMU100+xSmkbTV75cPf5UG/ctKCznrxln8oJ+0hKIt981CM+ssLUbcWaGVQmjEIpXDtm6kULddtG73Hw+hemLYPjKUJky6u279YVHpBAQtBNICy0LKJ7GnPt01q8T+09OD0q11Tr27Folsqb5NcjljnKQqocPQyWQpUNvzkv5aA9bB2IP6ITxKy40aoRKQZdG18ZTnDNFypDjlmdT+kWBfa9hSn1adujpsvOtt9vqe2WvgOSSuC8Xn5LdyTqMamfkZq8xWxiDSf1rHaO4UXbdOuDjRO+ctzAr85xpsoGLLrBVzJK+iJiXfW90rp2a9uPBQLW543Ru6ZWdJodmxW3y2hxnUpRoA7tMLtmqEsZxY3rtdVP/IsZdmOX3dYCBw0MMRkiFo6Y0xzpWmiZLMF4rbdCdBVJqNRTRoT21/yraLwF0Wha06vzpO1xjhN/jHU4CWiQEHQTCIuLpE/29BBaAawvY/0tkNKA1tUwevQz0W9zaRs/ctzKdPnZZml7T6AlyJ7UlqWJsj5LsBHCbwhCn7Eo6G9oDzOIwyzUEZajQWsTf9OFUIVCfyEym24A4fL/Ns+7lWNgBVPPAbX1WAgSdPXHlExdxH61c+wq2RM/OkSylhu2ORHunhs9KbVwZ+aJxZI+pDnmhFHstPkoxcCvY6IIz/Chs47Hgw/nyQFlQrAbuKJ73Cwy4sCwLOHiUuljy2E3Z0oLg6zTPgi4OUP6L5IJjyo9TJXn8JJ9OgfoMCoH+jB+uuGEWB+incPRui77YKRoiweC246M8LnbYyGjCv627m8q1VHoWEZKP/CUPX4hPQl4kBB0EwiLSQYpOvYkjDbmzYV4LM2RFvgp8kuW6+jDDGFF8+EceSGlzlGUOkdxtfRuq/MIsILZ9geavY8b5Tf8fzsjWIKtpUQwX7av8Lfucauffauuy3sKbp3x5AjmGaOba7MUDmk6wnm4sZix0Ebj0pwUJj651DmKp2zWXf1jhVVLkwdmrp2IZaLrW9uPErFMERVB372Ooh5/g7YdM2RQ4IiBY6F9BB6JQD+K7t9mYWauXiF9YLq9c6SvuNX2CuOkD0POHSf+yNf28bpC6wO2Z3nXfnvI8bOlhTxoe4aLpE9Nj0GNTqLHSh1OiA/3NM3c09gFVmtu+tEKtVHXM7AW68ckB+BTesUzHdYj9qf8f+eiT0y6t3hA7QtICLoJ7HV4xX5vi/YXL/c0q31EsxCa09rqjyaesWIAN9re5G/n+Xxi/0+LuMftjcgOErDMcF8GYDXeyOgZGPVqdf5Z2WjsSVh519w68WXhrsZMjLwPLguxa2ZdiwPH9S26Zo6HQ6RnaTX/sRl0F9aEtbyFt+h6zh2rGZeZOWnG8hifb0Pke7+/uJ6LIwhRg8TfsUb34xOyo4fZ6ze6l9HMO6M6QyXzCnCfN4OeAule2/OUiFsNQ0na6CjFfHl+9c5ZQTjXXnOsy2Ha9pWx+MD1XJe7C6WR63mvxcfg3hwwJ3yHzp001V4nHpkjwilKDxJLdY/HIy9xAuaQEHRbJVqLpqe1jGPPIh75eyN9pPQ339YRi4tdtG5XkdBNXMe/ztGGCd1bI7It5oTVwjw5lzmiDAidI760GmZhdbNuWdCN03rRTmhednU9i64Vi12468wVzBPtWBGSrJJIBfejLq9/3Ag3a2L+W1pp0ZatfOyYzFR5ts7ZyBYq45Qhxr/0IBoKILGTUXlidCO//ZHaSafGsoXZjDVZDz4mdbNvvLHrsvUYXbM43gQJlV7/vvthZT27weaLj9fWUThH+jIoTKCbsJZS56ggN+Vo+lQjIIyGcV0WorPo6lmC+4l/mq4/Sv4ypK3w/YXCTGiXmTXc1/b1trdCjsWytoVb943GdZPtjaDfvrRDCcQfCUG3FWKMND9ubUm4GCt9GDaWxgiXSvPiNo54wyhmrTnwsP3pmNvIikDsE71jXDDMfEj0mGQ9dSMjUsqjcFjuvAx5L3FlvtEEqVc4DBbNxTCqP65Gz0WvrJV2fTDDShkLbpVf1j3eWbQWf57ezAzeVpRKbp1PpBWrrdVxGCEWpYNxP5H71xKrWV2R0qnmSdtjUSu5rpLfA/S5B2JRrJi79/EJAzHjuhwJZoRhrdBq3rfH+vt/jN9CHqtF1xyGqNy2zT73cIRjk22vGvYfOdrVgwyqed72QBADuHZsA8WVTLc9z1R5Drlsp9Q5isdtTwLwqWNioE9T3h2xWXtjhVUPEF0yqijHmWRgdW2JsLKWar8lOEH+V5EQdFshtOlsYsFq5xgm2ebyheMmy5aSW22vxG0c8cbLLey+HCvutz0T9nxLWnTPlb8yqBvZvTjPIN7ELP5xnh9T/ZbCKIN7ZBZmP26Hq971SKlorMSR6s0no7kRva0qGN0N0m49YLNGUNbcVkO9TaWh67LOWO7QtS5ah17bVmEmvjRogyroC8Dxcl3W4gnbk4yUfgyKXZRpoo9gjqzP9x6mCKHEWcbie2AdM7oq9XUMMkhZ11K2awEl4rvteT7WBN2DRf3Y+1Cbo3VYtYLFGqM7Tg6ktIvnXL1EDiXbNGtdHSPN50jp16A2fD1msZPzpAU4vQJaG6GKA0WPBbeLGKrQDAjX4YRZY1h5HvHJhxv5GRwkhP+eBSP8/DBKZxat63Jw22ZVG9Eh9gAVT/iGR/me8LKMBglBtxUiXhomrVCyyHFdXNptDegmGrMT7nmEPr8MwbpF4xr53bj0bRZmFmQ9C5dVmN3kWkFPYTXDxF/j3m60MPsO51oQXp+0PWG6rDWXW2On5j0BqwKgVeWLFddlvXujl14rOsTDddlq+ehdoMP3qI/DJU/KFLWAPVGeyzuOqboMs+/Zp3Cq+E3IcUlno2tErnOOFFBSmXFdvs0WSgSobj8c4iVYPGd/KOx5M7G0HqtvAGbWlmhdl8PlZDUaWwCKwd/RtGWMWEmXzIaS6AnxvzrHMc32gt9LJ12o0SVmzKHKkxda2BVUX82X4RPyYo3RtVJGHROvb5mNjBFS6PfY6nplpb9oPZb0YpDNwOw+Rj2ucDmkw333PnZM5mr5fc6WFpoeXwIBJATdVohY4izVeMw2I+q6RkxxrQmxxlE2F9p645fUiLSQHyX9EnLMyEIWDrHoJFuKBfAdx9S4tne+9BkfOKYw2/4Ar9nvirk9q/lj9dAcuuFMYVfkQl5cq6Mk6S2u1i0brxjdeMHqBjUSW7WZ9o16dAjRM9L6YCSIW2k7XBSqfnn94weqSGTUJYxcA831FpnQRT3+rsI6AAaKvzPOy5RbRAUZVNNbXB3EWOqDFUE3m8jvSTTW7EjtGJcJD0GAQraFL2OiLxF3VBau6ARCq/GeRq7LzROeEwvMWFeDywWgrSN7520/8S/usf1fSBvfOa7hK8cNdBPXBbV1ofSZv4xv7p8jLzQcixVGZTNlxqvyHuvVa05G5e465E1mhFhzMbrGFt0Tpcgx3WqYDU8SUMhkF9dK75AvGO+rzTyXfoL52OgEAkgIuq0QPi14rBgorQw5ZpT4XosB4qq4jKE5Ec9Y5nhCjzmxpViHD/B+MKOBuSih+Ihw1vKWhoPCnbY5/l8DxFW6eQ6t4PEYFEQ+3OKNAYsnrFjTDxXN3994uS7HC1bnmNUtVweD1CzNhSyTCorMMAJabGRUgfs5RRWOoj5+i8k0ckbPRi9Ni7amto3bbS8x0fYadhr5znkt3zquNaytv0GN1JNxKfV1GCmW42VBiwwl4rttRiA1+9ZImhzC0Vyl756FKhn0lc9GrstH6lj9IkHdYzhlt5X10kGDivDI04NTME6lBaB42z9TRSK0n4Y0SW2lyxN2hPYrBFt5PfdG0dwvfQSTR8Z3/QrmFNF71vHtT92ynsAZbUywOUR3LWY9jwTgTttsrre9xRAxdG9/oFDKyeIiU6PQI8XrKaxuFi+5fQkJQfd/DE/azbk/PmF/splHEjsm2EK1jq0BeotyvKz0zYl0E+7V8RJ033TcGZd2egmhVspvHBNianOYtCym+gDthFCrfqwwR1hiHRNtr+ke17OitQSsvilKHD5jqToxoPGC2Xf/P7L+c4DoXJd7Cf8g4QqZN1Pkl+gsbAhp8Szpq4hu4IYW3QjWaRsuuuq4KvvGCuGfgZ6i0FdvoCbGVj0fzKRSMRZ0I8Pcehg5tjbSptlcGiBz1lk1C67ZOnrj8dQOxllhXCsdNPjT8Khho4n75Gd1594E+U2/1d/ft2qzf4BOCJOTejKotrSOPGqbwZeOG2knlPsZdO+Vnw9bx9e+mnFX++2wGoYhoFDqPI8bbW/6jxkpyfXcWOPhYQDB49Z71tF+iaJV2Om7TwffFycNtBO2hpQLJnwMJUZMpc5S+Es61fQW/gm5107qDXKJK36PGT0iw3mOSTxmn2nyPQy9Tx84psTdS25fQ0LQ3UcRDctyAvGB3sZpbxB0H7MFKzcmyq+y3HFp0Id2Px0ijegR+z1533Gb7vFoLejqNBCtDfEgL7KC5rBKm0G4VA3dhFB3fqt5IfWg51YYL5h18wunWDB68snUM8t2Xwgb+kFiKe87bmOC/Baj5GD22UvkT5hjvy9kY3W/7Tlm2+8P6eNN+1RKnaMAYxdn9dzsIqxnkMat70r5Az5zTKSzsCHEsmXm/uitn756WquY1ekQe9xyOJizxManjPWcuOYsxcbWdG1dAY/iIUMT/iEAL9qn85PzypD2jhSXcLa8kDtss0P6GS+/G6KIE1DoIGyi1DkqiI3Zh4/sk1nmHKt7XR7X/dDr8eV/fs4WiJXuEYEc0IxgYpXTooNObtUuwnr/+6dGkupb5RvJadIiE71YdW/XayFa12Wj45EE3eDzIm7WOEczUeWJ8rTtERY5xgeV02Z5uNkWmk3hI8ckFjuv0hxVmC4/q+t59rL9Xt5z3Bby/A/0fpu0ucQFzH279eZTW7YGZRjRroPhQwwVXcXS/yISgu4+iikGqT4geIHc26HV5rcG6C3a8baOGbEQxoJijTZ0nPwR6UJNEGnMgxHYo63gKun9uLWlhV6MkRmcYDFOxzoUvxAeKa6xnVDOcBXBVksLulbcn2PBMeJiJsoBofpF23TDsudKX3KyuIiP7bdgJk7QRhP3yM9xv2xu3nYXSkM26UYYIi7nDnlWxHK+9aCbsDase7JxrlbIEapY7riEg4R/g44Pk5YyXFrGLXKwUiLPGwvWXSjlZOk73THpCgLi2qB0KQD9Val9LpYD7Ml3yrModY7CTmPQ3JzvuJlX7ffoXkeesCMkvEJvvTxO/DFoc29FUai+rkxB/1kGuy5Hv5bGw8NFMOG6jAkh1hyFUmidSO12FDbTQzPvkoU6hojLQ67fITQw1z6NF+3B77CA4l9PtCN0RzHyR73hJVfIoS7z+3nTmWnHdrT4E/Mck3jPPsWw3SzVfIk058zMSJfFLbaee/Mx0s8hx4aJSzlS9W2wIniamfv6Ft3I7v6REK/YXp9l9BBVPl9ZCL2Wf5znc4dtTtAxbU7ebJ01YrS0gHPkhSGeZyJuw+wI4bgufPfzGm+6ND2kqgjIfC1+6xzPEucVQW2pES7md7S0gJ+cVxp60vwvISHo7qMI537Z1yDtgA9mEq23FjwQo+Al4KbUOYpS5yjOVxFAxIL2Ommc4k2gca6KWTReUH/01KyPz9ofjntfEJow3SrC5VIeHxVjtfVUOFbxvn0K/zpHk0QduTqbGieBuLDP7f9hlv0BPwNmPBivWwK+98ksnrE/yjj5I/8H2SYY56kVcfOYfSbdxbWcK3lcMMMpADwWza84S/6vYRkf2gub+dgxic8dN5sa90v26Vwgz/fHXRkpLgQUkqjjE8ctLHVebtiegJvTxK+ZKIfGyw4RfyNdqGWs/HHQcd/mSrvxvETypDxJF/RzEueyI+YV6XzZw5EwWlpgWuDT21jrbZq1likRN/lUBrkmmkkJ9ZBNPwd6S8XoCihBDLZGiOy6HFmobi9sIUejpOirIa/RxrQeLy3mGfujQccKNMRYXzpu5EPHrUHHxsvv8pJ9OiWab51aqaMdv7aMD8neNU87N9rrWDd99c2shcWasXX0ttdb/FevOBAc9xocyxw6b82Mwapy8kQdDhW9OTbbfn8QB0s3A+LKSLGbRrH1eu+HGeKn6F2aI50P7s/KfdXm4P7ScWPEOpdI83SPXye/pfplbkwCip+AL1yIx9HSkqDfx4o/hZTxPYtOwkYWO64M6/3kS5vWSdgUcu5g4W8uiNN+d2/A3rFz+h9ER53JGS88YpsZ9vxFKq19a0essZAzbI/7/77TNseCdt94izPHfl/IsVhIovSQL1SaKneMuJjjxB9NlVV/2E9VbTRzwwiUsSKSVTMchomRYmlbn7t4L+8mq71QrhtzqxZ+k71kKDfJHnerzmLomhDOQhgvpFHjd4GSdGKM1FC/P9oNsx7UKTdOl0LTyoS2H3im/b3a/CwDqx143CJ9CJfaAeAm2aN4yddRQGihnrdtvWvQUeIS3bIiCg4Two6IwsP2pxmnY6XyueBphTKfFUB73OndUBkpNe2CC6PtpVWFip1G04KuXooVvU2zdtMo4eZH59VBrolmmFiNLboBxBKjGzn+Fp6LoCg0E6N7mfxxyPzSKpMm2N4OUXC/7bgj6PcUg1RKavzgvCZiGR+uld/RPW7XKKvUz0rLreFj2T5IYylLQ19JI6CYspTurwmzMSMcqVMy+b6H/YU/WOMcHaI0MPN1OVoMtcaGQ3sxVEnuNLF2zLY/oKtc1EvDqB53J51viqdMdDG6l2oERG0Ig1mWeK3XofZ8c+da76hLWAj9wigOjNZNAegk6ittwiFNR0npuw9jpPnkCTt0heHAeDz36EidjB7vOm4PsXSDx3jwi2OsbojQ3oyEoNtKkYH1vKs+RNr8htMCQbCr2r6O46XFQb8nBGnsjBHPmFureUAB3IqZV1fhGfujPGV/LCRWRQ+ySkhRL9rxSLFihFOkb6Ou+5Bd31rjw9WSsZuQHroL4WOy4omnbI/obtb05lVhGKVGkeARJiVcnC5+3Qwu7QornJfyk/NKAC6UwivBhqpYJc+RI3sdzLPf4v/7CBN5kHNUG1Gf1e/pEPKPANSb3Uib/P4WXLXVKWzu9Lovh9Po36wimgpn+TXCpbLHQqtVxPmsqtE8d6PNolVBV8Stq7Rpp+PZoueBozfnte+GfoyuQcx2hHqeMuqNvP69axPhO6ntSw9dhPUhxybJrwT9zmMHDs36PFZjaesr/h0SixwNTo1hvdVDWyFYmWU3+M6YsY4XadY5o2d3gLDO8hyVabJsXfW9a4dJHmFtiKSNB47cXjxybmu9OMzCRpOuYqm9Kp7fSLGvJ+iqiZxKhC1M1gmPGykFW6S1IQzXyu+GCOQv2O5nhu2xoGOrnBcH/W7ZoB1jSILayh+McOt/NNDb4/megU8Jo7eHOF78gTvkWRR43ZrNKJB9GCouJ1uo5hPHLZEL70VICLqtFOYsi00I8g4E+1YEaRd467SPKb1K67OCRcKZUSbR1tMYh4uhUCOersiFQmTLF3g2dT6yJDMf7dPFwALXX4ycf0095zrrsBM2B+6zPddsbauZK82gOccCwdas9kK57mZNL5b7gDAxNr5ndqk0j4fsT3O7/KKpsYyRPjflNZKmcmHvLpQGWTz0cJXq/dEjVtGii0oQ9cXWhYNevFqBSe+GSHHyZiy5Pqg3GD7rlZGV6VRpUVC6tlHSF7rlzCjPjMpEk6bFqDfrAoG+RfI0MZQYR2/+7KdDnKhtT08YHSl9r7shU7Mum7H6Gt3TabYXgn6nar4XPYXVEa2snzkmhrSrFVzecNwVokycZDLdU2uDXswsRPe9NHL5nmJ72bJF7xLpE6yys/vWC5/Aod2TNbdVMVYsclzLVFvo92CIFLCydhdK+dUxNuj8eOltRqtSNx4grqOnsDoopORc+Ssuk4Ott9fJb9HdwIXahz7iPyHHjpCWcpSO1VGNPZXTXQv1ONpqOE3inY9eb031Weh9c1PNoO4joJ1pf5wL5PkhLtu5bOdXx1j2F0K9C2000U7YajmmfG/BvnlV+wCSNTncBFslyR0fI63bRNV/t5LaZTqpnR8idf9ppHWbRFq3ifx7wPP06FjC9OwsagT9l+9QQT9PbnO6qTYXoo2rnCJHduPSy3sG2s1RNAuZ4hd8mpAilrbRxCeOW1jp1XSaWZDUFom7Zc+mLRwRmfpDfonXgtTqIDSCtBtMWjfCxfFqcZBOovp44hSVO7goKKQKWvIJ/Y9iOAHQ98Hz5UG9UP484jjaUMVdttl85bghYlm1Mmig+DuuCJs7tUXnFB0SpOaAmfcH0I2JNoKWfEeLQ3XyjBttdC6UPw8ao56VBYLXFCOSvWMlY1c1q7hA0p8rVjfwEm7dOlKYWGs1HrI9FXEMekoKPWXch/ZJ3Kay3Iu4gwjdwBMHn0ydX4wSUDhMXEGNILDL+730xTb+Y7P5n8pvzkvZKon8bbMBnrQeZ8pf83WSk2rVd3ajLPF1klN1LfBuagrbxcCa/bPTwSJVmVpB4PGsDHaoyixKcvJmWor/d50gcHubbDZLgbm0IDmJ5zPS/b9rBIEJeW2CyryYnsbL6Wn+33/bbNycm+O/fjdwW5ts/rDb/GXeTU3hoazMoPt2V05WUDu+cd+Vk0WpLKPFZklirfd4B43y3UzASjieEbfFVGtZQrV1BY6XHM73rdXOwZYmB7QKn+JOIbBDmWW7DwV9VymhAAAgAElEQVSo8M6zruJ6soRqqlVzf4LtbeyCi/WyRCMegqwPHFNQwP88fdgmilR627rO68a+UZaCnm+dIPCvTQ76sqnnhm+Myx126lTvUZUo8qd3TvYR/6aQbdQKAjtFIaje8xnp/vdGAU5pW8CUNtn+MhWSyOjCfLZKgXdrsdMRMpd/ddiD3jcfPkxN9r8b6jlwrvwVa+XAdYVzXY4G4eaXnuIvknHiSOkXsoRqLtLxyrpDnsUix3jSdPYk+wJCV6coIAjCscBjgAQ8ryjKdM15B/Ai0BfYBpytKEqp99wtwCWAC7hWUZTPzLS5r2Om7TF61j+LPfczHG0iE6no4ZWMNF7J8LzMN2/bzuidu/yvzonSd/zY1C2kjo/NcJ+A0IicuhJb1k/IKaGaxLuAN+sLmLKtkp71gaX5SHEJC9x9ATjcIA5UFGtI6xpwy3G43fy8dj0d6oxTsgjSblI6P4gg1TJyayb3Vi+nc91LEdywXNiyv+UofkOpCSyaeoRXASiIzg2MsL+Hu8GjzfLFd07WuM2pIQkK4ELO+JXjcwu5e2slfeoDRCHNRYY0UPyd793dVUfcSMn/4ix8B9FubK3rQYn/78Nqarln6zay3cEfgHfttzG04TFt1RDEwrwaFkI9qV3uRZDq+BT4lBJ+WVOGDXhYJ1ZeFOpJO8BjBepBCT+UriMlTP4cQWjAWfAmD5HJtdt3YDMsGcB0C4ohtatpH/Fv/lGKwpbvFYbopTlgpxGXSUH3UPEPQyFTi5vk13m86VTD83drrH0QXvmkdqGWDeZakoqI7E55tolRxobzDZQiRrNNKzD6IAoKjTqCh1mmebWlSMGXjsNzLytFkZ2iSIcmz3NzATMzMzh35y5/mVkZaSx2Opm5ZSs9xFJ+t9s5p20Bd2/dxsnVu3jB/gA9OnrWih9L1/GLYxwvZzrok11C54YG3tuwmRcc99Kvg6fMJ+s28A5TGVOYz1KnA4AVa8qoFQSOKGkHwBObtzKstpZr8tqwMCXZX6ZOEDimuC0AUyoqOWtXNce2K2KjTfaX2SUIXFSYD8B95RUcv7uGQzoUA/BcZgYr1pSxWZK4oiAPgGS3wgm7a+jvLfNOWior1pSxTpaZkJ8LQIbbxZm7dnOot8yClGRWrCnji+QkHsjJAuDgunoObGjgtHaFnutMTWHFmjIOad+OelHk3bRUlq4pY5MscVtujrddN5dW7eSunCze8AoGJY2NDK31KEx9434jPY0vy9aT6/I8c98zAJi4rZLzdgbmv+9ZdGho5MMNAa+SH50OLvXel+VryoKUHQ9mZzInI51Fa9eR4Q54ENQKAnfnZHH+zl10bQi2AFeKIqvtNvrX1SPhCno/t0gSTkUhwx06R39xODi4vt7fu2+eVTh287XdycDaOrLZ6R9fnSBwRtsCLqzayRm7gkPOFjsdfJvk5LrtVf72vkty8llKMlMrKv3H7s7JYmhNrf++LnfYubAwn3nrNlLg8rwfA9q3o1YQWFq6DgH4OsnJVQV5XLajimu3VwXd2/fXb6RTYxPfOx2M9d7TFWvKGC4t85cZu72Ka3ZUsUMUGdLeM6+/WbueTLebu3OyeN37vFes8VgRe3rrnbSrmmkVlVSJAsO89b5du450t8J92Zm87FW8+Or55q2vXgNwVInnHVlQtoF8l4sr8nP5NjnJX08BBnvbnrZ1GxOrX+Nm+XVGti1gnc0GXj3jEqeDx7IzWeGw81h5Ba+lpbLabme13c45O6vp3tDAcO87e0RJO1asKcMNXOK9J3WCwKVVOymVZc4vKvDODZmrd3ju54UFeSzxKqSe3bQFZ01gdfTdD9+YfYJmvUDQWkJDoM4mSeLokrY8vmUrw2uChcpqQaBCkujQ1EQG1WH3W2dJoTJBpPXWp2w9Vyek6DCvYrWfCc+/vRExC7qCIEjADOAoYD3wkyAIHyiKslJV7BJgu6Io+wmCcA5wH3C2IAgHAucA3YEiYIEgCPt760Rqc5/GyiQXaZ0mmSusKLTdBgNXKQxa5aadjifs+pw0rhqUgbO4jgcqKxgtf8GtTZeElDssDjElewKFjpVUZqzGnmMcg5S9U+HYJW5O+SE4igva8MIBAvMOU5hds4mnbI/Spd5jFWjSeUWqBQF713uQXApjP3EzfIVCvQwjLi1G2Ky/2CxITiI1706O+1nhogVuoIKrT2jHROfLvOk+QrfOwqQk2rSdxJS5LvbfCMvkIrKOq8AhNXC2vFC3zhqbTGH+RKa+6iJtaxZ/ksXmAbUMa++J18gJ43r6fxlptGk7iZGLFc6eIwA5vNxZ4IxeGzlOXMzH7gGGdWPBc7b7OTh5LM4Cfbc3ABSF9uVw2Eo3w1YoZIaEsNtYayvg2T4C/3ZzMa2hnFy3mxJxKwLuiG5reky3saAJSGs/A5LXIboVTv/GzZmLPPPuupHtuD91g58F1IfdgsDmrs/SZYPCtBc9m5snDi3iuo76uYs3SRJrO89hyjtu+qxO5qs2yQwcZuwuvNjp4IzKm2n7uZtVFNHhuHJwGF/D3MZMer+9nVUU0ebgKo7bfzGPNp1uWH5NtYO6jzwb5Kyu1eT32snBgjHDe129wJ+f52PfLZKUV0/JkMoQ91A1XC747bs22DfYkZNcdDh6KznCThoNBN1GF/z23zyc5TKpRXW0HVTJEaK+i9z3lWlkfu7Z2OX1qqKxi0QHA0KSH0QndfMyyd+RiWJ3s9/RW0HWF3QV4GqxkKtf9WxpkzrXkNYrlH+hThB42b2VojeLcO1XR/e+gWf+p2zj9dI8TvleoetxW3CkeubGOlnisfJCzv/CTacjy0nN8giDuwSB62vbcv17bnIOrySv0LN5bgLOE9tx89tucnvuROwUWNtOSirmvlkupANq4IDAuF6X0+j4VhqO1Ca6Dg64JW5H5M3lhQxZqZB2VDV16R4Lyg5B5Ok1hRy/1I3zqAaQPfdgtNyWq95V6H3YVpKzG6kRBM6X2jH2Uxc9B9eAE27Pzib/mySSnC4yOojUCQLnJbfl6g9d7D50O92Ta+ndoZhTv1N4b2sqrgMF1thkXiWT615z8czQLMZJ2zmnKJ/RX7r4tzETV5fdfJqaTEGlwvj3XVxxTCFz6jfyRHY+Y75wISoSdZ1hWk42hdsUrv3AxWUji/ikeh1LnQ5Gf+nC3ggVXUUezcmkTZXCjW+7ePzoNgxjHQtTkjnxBzcdtij83M/B/NRkMqsVbnjHxctHZHEW1Wy0yZzynZu8KoWfejq4OzebzGqF699x8fjxOYzwks6csNhNUaXCvIOTeSUjjZwqhWs+dPHcETkcLtSConDSjwqFlQqLDnZyRWEeOVWeMb9wdDZHip4yZyxSSGpQWNXVxnX5uZSUK5z/hZurTshn+u5yBLfCuHlulnQRwA71osiw5W46b1KY3yuZifk5ZO/0tDvrqAwuZSdvpKfRY42bKz92M/3MXIayjsVOh6etT9wc+qfCqAvbMr/K4xZ5TtsC+vztZuJbbt4YnInSrhoBmJGZQWa1wqPPuvg3X6RioEgbwY0buLQgjys+djF8ucL0M3JJ9r5Pb6Sl8l11Gm/MbOLNbkVc0mODXwgY1K4dk1934S5zsnrkNv+OtlSWucVVwG2z3Tx/EMhdm/yCyMyMDBoWp3LMrwp/Da0mPTvwPk7IbUPqcie/b1A4q5envAuBckni25S/6LAwh19S4eJOn1CueCzeh5S045oP3XRfmcGSE2CYYyngsUhOTMrjrpdczBxs56rMrTQCtyTnMvENF88NyGFs8jbeTEvhr/IUuvyUTPkhm8kTXYwuyOeqD9082LaQB7PXMy8lmcxKgdvfd/HmiHTOUnZyVUEeY75w4W5KpaFLFX847WTtUrjpLRcTRxTyBusYW5jP8GVuDv1DYdVAG7mKC8mlcNPbbn44IA3SqjijbQGdNilc/LmLa44q4MX6jbyelsrpi9y0q1DY2lukRhIR3QpT5rr4b49kSKvkpHZFdNqkMG6eizGnFPH+zg28nJHOUb+46blG4bdD7NhEBUeDwg3vuFncNRkls5KxBXm036Jwxccuzju1iAU71vFtchInLHZTWKnwV3cbPyY7SalVGP++m9cPyeIkYTeioKDskOle4+YnYIXdzkWF+Qxd4eavgiTcwD1tsumyXmHMly7Gnp7PwvJ1/ndCdits6CTxWUoyaTWed/T1oZ75fWJxEW0rFG59zcWMkWlcTRVNwJIkJyf+6GbMl27uPjuXm73C5GfJSSTVKdw/y4XkgtdGpjPD7iE27dehhIP/cXPLm25mHVbA9UVv+L8FIwuLmP5CE3nlWfx9guKfr5skiYn1hUyc6+at/d38p8erfKv0BOAP2caCFXmMWKZQeFgl5AZSKc1TUun4ejpup5uUETWoNd1z1+XS+1sbOd13QRdoUjzfyS1uiX8+yyO7SmD4iF/4Kq2PnwQtp3YZqz4qIim/HvnQ2DkBWgsEJYy1wFQDgjAQmKooyjHe37cAKIpyr6rMZ94y3wuCIAObgVxgorqsr5y3Wtg29dCvXz/l55+tsdy1FMr+WskXt71GtrkUjRFRngF5e5+XcQJ7Keps4Gw+TipdxGOO1yQpbE8WaGsuDDqBBBJIIIEEEmhlqE5WSK3xKAx9nh8JNC+GzrqSrNyCPT0MQwiCsERRlH6RysXDdbktoI5uXg8calRGUZQmQRCqgBzv8R80ddt6/47UJgCCIIwFxgKUlJToFWkV2FGxlYaU/mwODQGIGpudkcskkMDejHjMcQnYnB97OwkkkEACCSSQwJ5BdVrkMgnED/U10Wd/aU2Ih6Crp1jRmomNyhgd1/Mz1DU9K4ryLPAseCy6xsPcs+g56HDu/rM3q4Qmjl2icPF8azGBcnIT6SV1pOTXk9SmAckW4VLPnAPdT9E/V7uDfx/qzA1iIWcsctPn31Z726KC5HCRtV8N6e1rsKe5MODj8uC27aAiAKF2O5sf7MwlziJGLXTTZ7X+vcnvU0VWl91BbbuAk5OLeeCFUBIWR1EdHQdXIqi6UoBeHYq5cL6b45aE9pM5aDuFJao4jpRcvnbv4oasXB54wUW+DrdO6ohKinODCafcl31FrwUXkLtD4d7ZLtJ1+AZWtYNTBmxClFXjOOFh6H8Jbz1UxN1Z2Vz/rpv+fzffXJGTm0gtqCe9pJaknEbESHMcYGoEk29dFaUPduJrexJL6lJI3ixzYJlCv3+af87bUpsoGbYNe6pmPkzezD2Pd2PkC0nYNKc6j9wSXP6W9Rz10iEM/V7gjG+Dx7y1uIkhA8sDc+qAkfSoX07X9Qp3vaQzB0+poJPTG4ue04Ue6fVk7FZ46LnQOTHjBJEn07ypUVILmFTciQ/r1tP3bzf/eSt07XrqeJFH09d7Fu4pFTz29WSeL/uE/EqFO15xhXiyfNtNYORLX1KQnAeiyPzfX+X6n+8lqV7hio/dDPgz+Fpr7bDxzXsZ2dWzpv2+/jvO+eJy5CaFMV+Gvj91NtjwVqD89JeH8YprGx03K9w3K/je3HeGyGzZe62376DHiz1BUTjqV4XLPgu+1teHiExuu97jIdZhCNX9LmDgkjtJqleYNscVEjby5Gg3M5q87uSdj+DFrT/xQHYmJ/+gcN7CQNvvDBK4pXgDouCJs+uyXmGa9xku7yBw+oANrHDYuTQ3n5cf9ByvscOBp29isyxxSlEhr90XuK7cczeThMKA9u2YMdNFrjdioXJMJQMa6+jdsYTx77k4bJXnvi24eDdX11TRs2MJI351c/mnnrFNuEzi013r6NGxhMJtCo896+nj3YECk9pv4IWMNB7PyGTu/Z7jv7UXOH3gBnp1LKH9FsW/Fr5ycQPt7A28Ysvg2Sc8x34/bxd3FWcycKXC+A/cbM6Ew4/dSK+OJcx6uImUenjprCaOTtnFw7ThvlkumkTofuZGenUq4eFnm2i3Db47uY6TU3ZwfnJbHnre0/YBZ2+kZ8diZs500WYn1BxXRc+hJ3P2qm+Y9qKLGgf0PXUjV+TlcskzNlLqIe/oCrJPu4mzFr3InS+7qLFD39M28kZqKu3mppO1G4qOqCDjjkUc++LpPPJcoK+1h1zIkkc/58B1UDhkG5l3fM6xc0cHlVk36Ar+vuN9iiqhZFgFKRffw0lfPcZ9s1wogkK3szax1iZT/lYeaXVQPLyC1Ke2cvL07kyf7XkeB1ydzpbtf7Pq43wKdkDbIdtIf66cy+7qxvWvgNvupvvFdtbWbqNmTgYAnY4rx/HINq67rjuXf+rGUVxHp8MqmeNI45A5Hsmh65kbqRp4GUvv/JiCHVAyvIKUyZ8w5qUxTPKm2+x6xibEO7Yy7skDGf+UZ+uY1beKgi67YWoVx8/sxkPetPSKoNDtzE0IV37LirdH8/2XDQz53TPXOp+8GXuSm8qiXlxUWcn02Z57tKF/HUd2roTJW+j5Sj9enx6Yz13O3Ig8/GZuX/k8/5YnM+kNz/147QQ3d6Rtpu7IOzj07+eD3oEOZ28iqU1neqQ30K3Msw4BzD1c5M7C9Sgnz6Tn8un0/NfN8T8r9FmtMH00zGnayE89TuKSXb8yZa6bg9YqzDxB5Lb0jeT+p5Qerw9mvw0K93jDS549VuSRzPUoQO/2xYx/383APzzXKozayn7uRvqVFPvfkWdOhhvSyjkjt5DZj3iObT9nOzML02m33MYFX7hZ0llgdP8N9FbV23LKTtIyGnmkLpfJr7ups8HBp2+kR4dibn/VRfcy+HNELSfnbmdYTjFPz/DU2//MjcxPT2bl0iyOW6Lwe88mzjiwnD7tinnFu5YUjCwnc/jFnLNkHlNfdfvn7BdF+7Pt3d30LFXY3qeWQcf2pk/DP/56JSO34Bx5M2d/+7L/m3PA2Rv5b3ISmxZm03uNQtXBtQzouj3oWjoevwX7hc9w9gd3cOfLgXpvpqfS5q10CreDfNhOuoy6nGG/v8FTM128eITIvTN/Z9tdbbipNp9rPnSz8YgaTjh+NH3Xve9fF/c7eTO7jpnAxM9fZ8J7bv9a9Z82OZz7tANHE6QN2067glqGtinmmSe99U7cgi3FTd927fxtbd6vieH9yuGaX7jmtaM56VW7f43vfOIW7CkuuPFvBrx8BLMe9dSpl6HX6RsR+l/M1es+RP7LweWfeO7pNyfWMzZlG9UnPc7gpQ/47wdAh3M2kdTnfHps/4qibR73foC3BwlMLtnA9swSDs+CCxa4OOEnz/yacgW8tX0jg0ra0eASeOkhT52NWdD/uE0kuxUO7lhCv7/c3Py2ZwyTz5d4p2EdL2Sk8WhWJk885fJ7zpVduJ1j6mr9cdyXt9em1No7kXBdbkGcNas3q8TwTJSptlS+zzsWFoVPMh8RkQSAqZ6PoBuYn5zEjV5Siz2B4rRiBhQOoGduT7rndKdjRkdk0YIOZsls+HB8dJ3r3SfvvdktCAzwEin4cH3f67noresMm1OA3h2Kcask4CV0wr5mYWjh/Y5E+WdBEKmBD4u7XU3SvJuDDxb2gk3L+CbJyZVeohI15p8xn4IHugYfPOMFOOh0mJpBzw7FKDpS/5QBUzjrt89hhSYdz8kz4eDzYGoGyx12zisy78IyrN0wDi8+nEFFgyhMKUQIp23w3m/LGHIjjJgSudzWv2BG/5DDDcBHqSnc7iVeiQUXHXQRlxx0CRkO77WEu6apVTA1I4iwZfaxs+n7lE6s9n/Wwn3tOaR9O2pVCpmTOhzHtK80OUmPuQfXZ5PorTOf7hhwO6fNVcXk53Wnofx3+uqUBVhWeCrid14Sr07DYeDVDP7mWqok/XjYX8f8inyn9z7evgN2bWLyi4fxQVqqbvl5p82jOC343frkgUJuzmujW37qwKmcvn9wfPAv9+ZxQZG+qf6mfjdxfvfzAwd2beak14axxh5K1/X9ud+Tek9bSC2AG/+Eu/LoUaLfrr8swEFnwAEnsO69Szm+uK1ueR8hCwAXfwYvHMOA9u3YLQbrcv+vspZDqjzpKtbJUlB7I3bX8Gh5BRAgmwFwut38tHZ9yHF1v2aPL19ThgBB5EoA89ZtoLjJxU9OBxcXBu7JoJpantmyFQVC1q8Va8r41yZzcrsAcdmSNWXIQC9V2aVrytgqSX5iGl/d4cVtqZA988xHcOQbb4bLxaKyDXyb5GScdw381du2r8z1ldu5qGoXlaLI4V5CmxVrymDwBB5YOYsXM9KZX7aBApeLRuCE4iLO2lnNpVU74YhbWf7t/czKSOe+8grs3nF9mpJMXpPLQ853zS/UPNmXr5OTOHJ3jcdaMPBq1vz8NCluhTyXC674nsanBrJFlmjX5P3mD72JqkUP4VTcOHqPgSE3wOO92S0IHsK5cYvgnwW4FkwFQDp2Ogy4Ingtadcf1muYt73riR/ZnWH7GlDcxmVSciG5DWxVsYcPuArlBw8ZpQBw2Vcozw2nVhBI9u0Tb9sOd2axym6jpLEpQJTnbf+pzHTym1ycVu21BF35A7w2ilvFHbyfluonSQKgbT+m1672E2b635XJW1jxQDGjvGvjzdu2M2bnLhg2CWXhPUHzzUfsx9F3c9zKGaz3MmJnuVx8XbYBzpzN9rcvZqh3HkBgTnPNLyx8/jCuKQjsffzkfweP4awt81nl8MyA/9u0hUPq6uGW9Sx+pLOfzAjg0S1bGeElFjLzvvkImHpoyIzU7/1FO3Zy/fYdnF2Uz0qHQ7etUVW7uKVyO5tV75D2Pb6gaic3Vu4Iqud7XzbKEscUt+XiHVVM2F4F/S/jw5WvMCmvjZ9citxu3Eg5DkVhWkUldDma3f/M56y2BdxaUcnAunoYv4xlT/fn7pxsXty0hSTvnHg8K4NKSWJqRSX0v4zGn55jXEEe11fuoHtDA5w5mz/fv4wHszN5bEuFf47dlZNF14ZGztpVDUNvYtuih7gmP5f7Op5B8dH3wNQMJubmYFMU7qqohMETWPrTDMYUFfgJthg+mfuXzeCljHQ/wZYL6N2xhP0aGnh3g0f5uCjJyRUFeYE5JoiUiwIjNPeT8ctwP9bLv375yOgAmFrFF/cXcJ13D/1V2XrauNxwyFiaFj/LwarnvGxNmUcZfMrT9FgWnGN4xZoyGL+MLU/24UjVmnhrRSVn76qG9HYMy1DYJge+wb753wj0MZh7PiI37fzTzkt1nSY8e1lbJDliD6MlXZd/AroIgtAR2ICHXGqUpswHwAXA98AZwJeKoiiCIHwAvCoIwsN4yKi6AIvxrLOR2twn8f2o7+GLu5q/o9xusHUVInBMTS3H+D4yR90Jh42ntqmWzbs3s71uO9WN1dQ21dLgaqDe5WEGdStumtxNiIKIKIjYRBvpjnTSbGlkObPITcol3ZGOKIQnAYoL+lwQnaCbrr85Ja0Idm0kRVH8bJsbrv2ZjhkdkUQJjATdtv0QNvzMstJ1wQL0nBP1yx8xBeGfBaxYU8Zqm8wLh4/jhI4nMKjtIFisk9NV8nx0h9TW8fXa9RzRoYQmxc0xHY7h3iH3YhN1OHflJP+fy0vX8YPTwWWqj/Tnp39OYWohLNdJJ6RSNvSsb+C99Rs5RbV57ZbdjZv630SfvD6e+9LS6HGmuXK5+8OElfDIgUGH7cBp1bs9m7Lr/4D0Qv85RVGobarFrbhxSA5kUQ4vrEeB7g0NrEgfDKeGplfxw+35IC1eu55SWeazE+/htC6nkSs5QSvotu2HhOdjqhYoZoyYwdCiw/BwAnoh2bDj+VBqP5C/jPkFcf7UwAHZAZKNRWUbOL8wj1+dwf7kS8cs9Tx/9ZxPL2JaRSXFTU3M0KQpWXDGAvJTQgXJ43bXkLa53M8268PMETMZ0m5ISPk+9fW8tmET57QtDDr+6PBHGVEyIriwZOeDDZu4MTeHz1ID8SPzz5hPqj0VJpaB7/1xN4bcQ4Bvz/3WU9aHvG5gT6W4ycVH6zYysjjwbmQ7s/nv2f+FzybD9096DpZ4yNy+X7s+aLN+WY/LOOSzB/2/i5tcHF29m8+943zEK+QCPLOpnMsLPfdnUdl6/3H1ffiuNBD1813pOgZ5lXUv5h8Na54H4KfSdX421HFFwxHWeMx2j5ZXBClKir1CWv+64HR3M7d4hHIBuGPrNr+yyNd3p8YmktxuakWRHnX1foHx3KpdzM1IY0zVTiSgwOWiW30Dqxx2fvDW/WzdBo4oacsVO6r8LL7vrN/E41kZPLjVcy8Oq63jmc3lFDUG6AJ/WVPGTlEkx8ukm+12Bysa3C5uqtzBTZUBVxgb8Pk6dVoOgZ71DUH3HODY3SqSNFEiWVGCjykKHRtV5C2ONGwQEHIPOh0Ud4Dl9+QnwbuxT1EUOPUZKOgB2/4JUKsNuCJoDAyeAP0vhWeGQo3XrNTzHELQcShUrg49rkZhb9ihyc+taJKHCSICBIRc8Hs/dWvQJ2y4YoeG7FCQQHFzd0Uld1doGPQFkYmV25lYuT2knR4NDSwo20C9IFDiZdxGlBCARWvX8VB2Fldtrwpw7ygKn6zfxE25ObiBh7Z678/+x5LldjNn4xYuKMrngfIK/5xGURhWW8utFZUsSnLy8Lg/sN3pU7QpzNhS7mfZ7trQ4B/zIXX1/jl71O4av5AL8PXa9X6h+iF7J8Bzj5/eXO5XzPiEjBmby7mqII8bt3muv7jJxblVu1iYksR12z1z9OWNW5icm8PFVYH7umjtepY77Az2MjQXuFx8VbaeNHcgIc0TmvnL/sey4q/gtDJFTa7g90NxceLuGk5UH0Phwa0qNxVBIkVR+Hi9Kgd7WiG96ht4c2MwEaKPCdrTjBsb8H+bVdkiBImuDY08tzk4H+2UbduDyuS43by6aQt0DQhr0zVj6l3fEHwtCNxcuYObVe+6hEbxCAyurQs+JojkuZpCyiGIiDr1fRhRU8uPpeuQFcW/1iFIyHjmxEsZaVy6Y6fKVVXh1zVlDPcKtP/d7n3HRBv5Lhe3VlRyd6aLrRgAACAASURBVJtsOjc0eIRcb50v1m3wr88Pbdnqmf8Zxdiq1gV9G5aqxnlj5Q4+S0lmsywzbnuVf/6Bhy37sPbF/r99iEs6nlaEmK/HG3N7NfAZnrn0gqIovwuCcCfws6IoHwD/B7wkCMI/QCUewRVvuTeAlXiUCFcpiuIC0Gsz1rG2drx/8vueP9wtwHZ2zivwRJ/Q44OuBSBJTqJjRkc6ZnRs/rHEimgFkANO0D9+2RfwcCD1UtKRd7Jf1n6R2xt2C7yiw0p70pPwWM/Q40W9/X92Tith2uBpgXNuHcu/aIO0Qti1iSy3m1+HPAkdQwWA4DrBAuiAunpWVAITNC4penNOU7dzo/cDEG8t35Ab4ZsHI5fTItNCTH5GW5i8GabpWKWLDg4ScgEEQSDZlhxaNt7Yb0T487L/s0mHpiYu73W5cVnv8/J/kNXPSTufvEoTm7ds5cQ1gEdAA4KtQV2P92yegRc3lVN18pN8k5FFn7w+FKWGTzc0bsdOLtmxk+8v/ZCilKKI79Hg2jqWrSnj1yu+RFEUDs47OKwSpXtDI8vXlPFbj5OoHngl/Qv663uDeOf3g1u3cf/WbdRN3kSSnBRQXjhV1i7F7b+HLsB9W4W+EmnwBHx+4+2bPO9GjSBgn1IRGIPOeyx4224ExNsqPdc3L1i7/9DWbbB1W4glblBdHcvXlMHR0xDWTA65DwybjLDGu450P5W0399lmfc6bCUBzwWnovBT6Tp2iQK5fYcBc+DAk5FWvs8va8pYZ5PpcNZcWBNQJi1bU8Yqu50uDQ1BPNenVe/m8JpakhQlSCj6ce16mggiAmVS5XYmaQSbNzQbZDuwqCyYabxLY2PI5n1QbXCIhg38Qq4uzHxXzShm7apAwbzuMOR62LAkcOzc1yFT5a1w2VfQtg8suEPTl+q75fBu4pUw4z9yquffc16FF46BNvvDaRpl15U/QnYnWDIrcGzAlcFlRr/jsQw/Nzz4uHauxkN5KUrB1+T9fuFID26/5zmw/LWgquoNuactzzuV4Va4s6ISMkqgyreh98y7B7Zu063Tp75eR0jx1Dl7V7VHmNC847kur6KksDe4vXW98+ONjZupEQQcGo/ILLVypXtf//HDtAIVMFTnmPb9sAH3a64pw+1miGbut3FFCIVL03z3BDF0rml/S/bQY9HOCUVvP2NC/FC/j0bvpt7xaHXShn1Evu5krXes915lud3BQj+A4kYGvvGtcyle5a73nvjnJEDnER7FVWOdrrDOgSfD908yqC50PtHlGPj7M+YHKfMCSHcroXW6nQSrPohwtXsX4mJuUxRlnqIo+yuK0llRlGneY7d5hVwURalTFOVMRVH2UxTlEEVR/lXVneat11VRlE/Ctbmvo1NmJ88f3U5q/s5yOusfj7PVqsUgR8FadOQd+sfTNZv3g0eba6+z1/U0u1Pw8az2keseMDL4t96HYeCVHqHZh6TM0DJa6C3Qes/+8P+EHmsX6u7bLOh3cXT17BYFUVuSR3AY827g2ODrYezC6PqPBed/AIeOgx5nhC/nsMC+YcV7YmRwaES2Mzsg5AJ0PDzwd98LgtaFjJJBjOw0MqKQS7Znntk6DmVou6GRlUV5Hou7CPTN70u/gn6mPAUEoEfXUxhYNNA45EEKKAxEINmWbGyhV214JQgVcs96EXqN8mxkNG0kK0rwGLTvsWqu2yBwfW7zlOYCIOjcF89x0eOtA5DryRMkevvyCzKDJwAeYff/27vzOCmqe+/j3x/DDIvMCAOyKCCyqaACiooaFQTBLYgLBkWDETS4R+NGNC6JN8EnicZ4kzyXqFeTeJMYY6Ixj0E0yTUaHxOSINHr+khUbowbqNybKwie54+qnqmurqqu3nt6Pu/Xa149VX2q+nR3TU396pzzOzts+6jzYtY/VzTLa5HtYU3S5X+VrnitYz8Tt2zxWiwuf8VrBfcN/Ogj70LvjJXS4oc76tMsSSfcJo08IPX7K8pB8cNJOqQJdIPfXd+B0jFfz37+jIek7QJDHU68zfsbzroxdET2Npm/y6Qgdtcj/TL+xfIe8dN4ddyUifp7H7ybd3Nsr094y23DpSNCI77GzpR6t2UHtgecl3uspri4jzRkj87fe/aSgjcfMnU/5UfZ+x8cmNMqzuRQp77MvkbPyP1sh+7pPSa9h6TvIzyiL3MOCHzmfZ3Lnths7OHp91+sA84rz36ierOFb3T0ae88HjOK7aEXdeO+uU/uurDg+TXufB1ZpxTXsZOjrusKeY08kv53hT/Xw7/gHWN9BuSWPe1e6cKnIm469PSCYL9xKtLYWenrmzE8b0/gLqcK/UqRxq9PCkziPHyf+IJpRP2xRDk81EX6snWlvW4tnfS9wrdJCpSuekva+WPSop9LfdvjywX16OEFUhf8OX0dmvyJTPc5PXv9hIhEYrseJQ0NXERk/pknGfWx3HXzIrrKBvebkQnQy/XPNc72MV3IK2XMYX5L2XvSrGuq+9oZow+VjryhvPvsmTApbvgf6w67SQvviS8fvliXpM/8RZp/R/xNsrAlD0tH3OAF9WmMnpG/TNCeJ3X+nhQYSOluCmUs/HHy8xOOTe5uHrTvmdnLux4VU7DAG4yxLQ8mjfMvuMMX+ZlApiU0bnrMYd7NuUMuDZV33v+S3m3K0ad/dit4xshp0ojQDbKWftIZvwyta5XODY01LcV20WO7s4QD3X6hVq5jbsp+r0sekaZ+KrvMyNDkDx1BbEKuk47vKqmM//13XMwmHA8dNyYSLt/m3iINHCcde0vCfvzj4YI10px/imjRi+jFkEqg7tsPz95vJuDpOzA7CWTW+w19Tpf/Vbr4WalfKC+FmTdm+NR7cz//xQ9756seEZ/RvG9733VifprAc2bpPvOT7gztogKB7nZF5lJJE7AG67vHiV7PgeDnMHRPacaV2dscfEn28oR50qcfzd138Fyxz+nS0V+TdirwOjdT57bh0euz1qU4n/q9lLIc8aXcdVJu0Jpp1Ehy8Gezl7Ou8ULfx+STpavfzurBlSN8PPUd6AXBrRH5JD5xl7R4ldQSM83L/kvjX6cBJ24i0K0Tg/qk+Eed1nH/kr+MJB10gXSYn8xn0QPpA7p6NO7w/GUK0bNF+tQvok+G5XTZy9IlL+YGD5HBn3ldkBb9XDr9F+n23+y3dAdbbKMuXJPssGv+MtWWL7DpjgZP6GgVzRVxoTN2lvf3f2me8XwZ/UdKE49LX5++7dK0pel7icy61nucnbIDTyFd1wtRzv3uMF6aeLw0/XPectzF7yl+t83tR3otKVGCn33m+5OkQ6/IXt9xUR763Mf7Ny/C3eX7tns354aEjp1yXahHff/9BnufTdBpP5VOuTu37LDOIR763Ou5QycO/mzuhXzm887Y5RBpYKBHwcJ7pHOeyC4z9Yzs95y5gN77k4qXeW8pAt00n2fmfSQFVJlgMalMz17S+auzL8h3OTS7R9Cs67yeUK3+sI3DPu+1cp37By+gCX5eV7wmXRUYXyl5N7EkaU5EcHDwJZ03n4MtxQNGeY/NfaWjvta53kyaeY205Fe5n1OfAbm9rCTv/ffwf8Kff3Pv+L/jyaf4PStivrOhe+UeT3GB7qjA0KFwUFFiotdIac6l4d5Zk05Rznvdd3H2cnOo7ife5jW4BN/D0seyW95Hz8hNBrnP6V7SzKCjvioddlXncp8B3ljzYJ0+frOXODMsquvyklXxZQrRKyJR4s4HRZcNv0aa7zZ8I3D6Mi/B51VvFXds5PS4SHjfux0tjdhPkcf4Z56O/h878gDveN49JqdMF0agWwcmDIy7OC1SIXdiD7nEu3DIN9az3nXVLte9+uXeqY6TeY+7HBLdUptk4vGdvxc69rQSd6bDZl5dWPnw3dKuYlIROfV2jBhLH8Us+oJQym3dsx5e+UMuiW8NO/470XfnK6Vni3cuOjBlD4LpV+QvU4xyX6DO/1dp+uXJ+x49wxuyMv+O3AuazIXr+CM71/Ud2NE1Obt7u3kX6lLuTY+R07zPd8cp6epdtr/7iHNzVLe+MYdJ4+cENmvybsLsHhjWEXWeD587Ln628/POWPTz7G7JA3aJvrGbFehmWlnTtMSWWCb8+kkXsZn9Bc/j5/9JWviT5H0vul/a+7TO5T2Ol656o/Nm6HaDpHnf9G5AHHqZ9zon/9D77Hq35fYYmbbUy3twwLm5rzXz89KEudnvSZJOuFVa8ANvDHPwXGU9vLHO4eAqSdZcfcUEDhHH9zlPSqc/EH4hdQQM4e/llB/l3//AcYXXLVae65y9FmS3+p75a6/3SfDzufY96aBQ8s4r/+YFYjnS9FQIGBPRK2e/M0PdlMO9F+QFyOGb1+Pm+AFx6PXC/+OKvvaL2C7unNd3YLpyiS/Xw5vFomeLEj/XOOHXTHOOiPq76D9CaooY4jNglHfsFzJcqosg0K0Dy/aLOsGUYFSFWyHrVTCYyyfNmK56U0owP3g3r8Vk6eOF7ycz3quSCv0+hkysTD0qrZgbSpm74YvCF2ARTvCy6na0tmSEu/ClOQb2Oin37nw9aWr2xn+el3JKuUP86bqSumxLKuoipOM1Lk1+Pu4CqUeT9InvRV/oH3q5dOpPpPGzO9dNPM5bf8B52TkErIf3vZ23urjxWWnqWqioYy1tK8x2g0JfR9xxGygUF/RkrU9TpkytteHuzcEW6pzXTxHoDtnD++7n39G5buAYaVyJ33eUXY9M7tWUZpxlcHxmn/7Sbpnu+1GftdIfdyUHuhHbDN7Nb4kLPZcZchA+luO6hgb3H2z1zHS7LbYLctjhX8heDicn6wjQUnw+wQRqGWmO60KlufEz9nBp4d2hIScxf/uR30GK/2+R3bcDdQq2coe/96LOjcFjvIjjNZxoL9UY+gJeJ80wjC6KQLcOTB6c8I+vGFF3a7qDI/9X+rLVSrRUT8YcFj0WN65sRnOf7ORElVCL6Ykqad7/jnmiiJsVY2em73XRZ4BXdlrSGJwGsueJ0qCULSbTl0mf+mX+YQ7BLptR07eEBXMi9Iuef7dDqrGkEdlvx87KvgAx81rZ5vxTdg8eM+8n/JkcWkTrd3tMxv1wPoFilHvauUJaS5PKB2/sdFyQp6pA/FPhMbp7nRRbNLLb+dm/88bcBes143PVz22Qke9mTlhcUBA+nguVdQwVc3GesE24bif+qzdFXSEy3bSDrYGTT5Euesa7cZVPuBVx8qnZSTcvW5fbMiuV/rlm7Ssp0E3Yd2IQliYIjSgTldRzxlXS1MW563dK0Qsq6jWC7zc8XjZ4wyfzGYczmkvS0TdGD63Ker1ytOim+BwLCVonHJvZKP02XQSBLhpHmovIjLiphdKIauUqNnNwvTr2W9nLmZNs1Dga5IpLtJH0zymc4CLtWHuk06OHtHOK7L/B7yhNq+hHEd1d44zYL//+ojKuJ5YPXtjGXPwXc8E7ePfo9ZNOLnBHMWN0U0vzPlK01qYpk/X9FHBBnnRBmZnaJU2iqYnzpHGzvUA2Y8jE7O7btXbYVclTzDWHZkCIDZZiWnTT5pAI3hwtV4tu55PZi829428snP8n6bw/5q4//DpvzPmIQAIzMy9BV1OgG/gn7vKy6gZd/Jx0fmif876ZvRz3tzAhOGtHITdrIiR+RgnHflIOkVRd+SPeW9RsJIdemh2QHni+lxE+zSwckTfb4s6lyq7vYVd5Y9vD3b3HzvLGP0deJ5XYojv3G9nLqf5OCnidzLARWnRR91qH5S/TqAq5mCvlTmfUiWDP+bnrkuxc4BjbYgxJ2XobJfz5zL7e63I3PiIbb7nk7VLq6xLjc+P+WSQcd+HkKUldHFEdaaYGi0pgVIqoqTjCrxO7Pu5iqoY5DDIvPfv6znXBpEj5pHkfwTJpui6nuZjrOAeWmFE5k5QmM/YtmARn6uLsqeV6tXpZv7cPZZbtCjJzgR5zU/b6uMSBsTdoUiq1RTdxeqEC/nYGjpEGRUyd1rNX9pjzoOD/1+Y+na2/GW3DYmbPSFGv4Jy5aY7hJKm6LkfsO2nmj6Tu/sd/J7Tv4HYpzmEtrV535+D3d+q90jn/t3M5c40cdSMv65hMaJXe+QDps8/lBps7JEyTVWqLbrhX3Qm35d+mqKCVQBcliDp8bpp+U8RaFTddjpS/61yjm5swnULZRH2TBV5I+nNZVlRJF92h97PjZOnT/548JqlUaTNnR029VG/i/sEkfSc5U0DkOaZ2arz57urGlW94GWDTtMCeGLjgGDQ+vlxamVbknqHxj7EXLXEtnmXswpil0H1FtOAUMt1TKmkuztK0+gYV0PKUZszewZd4U/pNXti57pgbpQV3xW/TlSy4ywvaw0nQYpPGlbHrclFjJmtxQR81Xr2A915st+RiW+lSJVlL+OyjpiBLGvueSbCW7721j+lMuhdVp+C+x87MDmr3PNHrkRC8mTTtHC9PSLC3Xu82L2AfV0BLZ9rvpBytpqnGeSe8TktM0iladFFuI1ojEgBI6ZI8RAlPDt/dTDktf5lg9tJiRMa5hc6DWVoVYgXHT5US6NZzFuthEf/g6k5coJv0uYa3ydcNNtAlblSK8bsjD8xfBp7m3unnMw9ecBWSDT3u7/PI5d5jTqtIipbKYLfiSl2wFHyuK/JcEnXRGrevVImmIsboJv0vKGj+W7/MrGs7nztuhZe1OKO5tzelX6PlI8gYsZ8X7KZ9f8X0OAhOfRI8/6U91o/5esptKnSTqGNfBe4zchhLmn1UsEU36UZQ5n1uFzFEIdX/wDzv7YI/SUt/m7s+c5Mwqt6zro3f95TTvK7mZp3Tu82/05vDeWFmyrMCbo5VRIW755fyOnWOQLfGYgPdYk+u3THJUlCaz+3IG/KXSVSGFt1KnRCDU0Dsd2Zh2wbHI5Y7WUxawQuRrixujFBbQgKZQlt0g8dhmimjMvOnHv7F/GWRXrE3JeO66mfG720XntIixYVJsKU0rktzzRTZlTpVQFRg1+VM+fB40qBU42/9bpCT/KRlHQldJE36hJe1uLvLfH5Jvc3SXu8MDmTbP/Syzt/Tzv0ZHBuelFG+0q1aWe83TZfcTHf3AgPwQqa2ipIqGVXS9VDCc1Etkh3zSBd5vsokoYp6v0n7zppD3C8XHgZY6jERfN1yjClP9RmlTLiWZn0XRqBbY33jLlBHzyhuh7UKUOpJVCbCoLiJ5NNKuoOZVqVaTINV2zMhu2eUccE5LGt0HOWbymhaxJyN9SguY21iMqQCW3QL7cZ2wHle8oz9P52/LNLr098blxic7iXJfmd5j3HZotuGeZk7T/lx9vqoeV+l+AuTrGzMMXVJO0dzlsDOorKOxpWv5AWUi13olJkiJqtoQp06bhr6ZT5+c26ZzGc86mNed8j20Xkq2h3FtNIFP/u4cbyx+1L2GMpievn0aEp43UqNb49o0S06YC2kRTdB0jCqpEB3n09F1CnzsilaeyPfd5nOEZGJw1K2FscGxKV2XS5xjG7i/mKEP//W4BzEtOii1ort3tSo3aIKkS9ZUclBZhladAsNJDP/VPIJZp4u9H1msv5OOjn+orrSWvK0TB5ySXXqUQs5cW4BLbppjr/m3t54uWCrP8pj6hne3LZpzPmSlxwl6Ybbvou9gDcoGLgGxY2bz/qeY46PNMm2woLHZZrkdJHz6BbZ7TJV1uUYOwTGT6cJujPjCzNl02RyRa40gUPV/t8kBNtx6yvRdbngfRbytxAl4XgfNzv+uaRANzNvc1NLxJMJLbqJ80iX2KKb0T7a+wkO10m971JaOsNJPP8p8FSJLbrlCECDczoXk0eki2q8dwREJkDwzbq29P2Xo0W3R8xFa5yoydyj7L2o8/dUE4oHDBrrtUocFzcHbJV86pfxz9UqAK+GcFfVfBnUK3Uxhspqao6fuiefs5/I/ftIc+Mi9vioxE2/uNeIKBvVyprzEilu6BSaUbmjLoWUDbz2BX/2poBBfpmx5gecV/q+Sp57tx7Pk36dCp25oUfPFLtOaFnN3GhL+nvJzJmddL0S1f0/6nWv3uBNozQ+EFh/8r7s7ME77Oo9jg0kptz1qORrujgX/Fk648HO5UzX+axWzQgdf+5laNHN+o4CzyV913HfazFdl4sZo9uW5/PpglL8paBcwofVjBFFdk9Gfmf+SvrOYbnrD8zTrTmViBNEodM67VxoYqCU/6B79PDG+I2f7f3eFcV17z3qq9WtR7VN/5z0eKB7ZL7W7cGBqQwOPL8ydUJ9GTIhf5lINcy6HHlBVuOkLZkLwKgkUnFlg++DLsrptfSNnne35FatchxDabpvVvhvJ3NchbNVR5UJfmapeuYk3GRqGy69+2r0cxnTzpEeu7G4QDP8uj2acqdRGj09e3nw7tJl67IT8Z38g/wvM/Nqaf3q5DKTF3rTdwXn4x2+n7Tlv7LLDZkgvfpEbnA/7WzptSfjsxVLyv1+Y27MhG9qZ20S2ObACxJeK4Wcc2+wVbmYTOVdE4FuDVm+E+iwydLra9LvMJhxt7vbKSZjajmCv6h/0HGTycdJczduwC7SxnXpy2d8/s3C6lKPTvuZ9L3QNEKFJtfqapIS40TZ51PSA/74qoJvnKBbiTt/FNUqFvi9kGAlqjWuoNZXJdQ3okV3549JrzyWp7z/OCBmTL3U2b07OCwEpSu1d05RrbMptynnePKs4z7zGJWMqsC/hYzd50rP3h9dPM1Y2aIy84ac/YT0j7c7lzOB8c4FZKHPKOa4yDdcTfKu/SaGrimWrMotd/IPpb//JXu+a8kfmmLJU86Fj8ngZ9sUc2OiOTz0JLBN1nR1BebwkKQpp0r/p4GHe6XURZt8uosCT7ZJ/6y7o88+n7182bry7DcqKUklHL+i8/ekcS2NaMwM6civdC5f/Gzt6lKv6rIbHmri6K/lSdRWqa7LaRRyIR8hTXfVqIv1pDm/+4ZaVJL+lqYvk07+UWcOA5RHMflEypn5NvmFitgmxb46d1rgPmpUh6jeDFEtmkMmSLsc0rm83SDp3D9480R3NX36S7vETNc3cV5y196kJLI9o8YyKzk4ThrXm+a4DM8IUPI44a6JFt0aOnXCqckFDrlM+tHC5DJBk08prUKNpnWodOUb0l8fk4ZPzZ56oxQ7FhF07nGi9HTMdCJxgkkexnTDbu77n+X9dFUT5kn/8bNa1wLdwb5Lkp/fq8AM7IkKzB6a2HW5TBdbux4h/fvy0MqEfW8/3C+S4vWbmr39o/rGzAytSBGAFtKtvOoX+wldl5Pqklgm6T2kCZATtu/lB7VTF3euO/tx6Y1n8u82mPytu8gJkItIYFXRrMd0XUaV7Ts0z5y3wXlN06CFJ1dz787sgOVSzD/HQUWc9Bsw+133UoWLqIMujOj6BITEtUKUmtCnkEykhZ43E+fpDNlxSoF1ynmxIrZByWZeI234f/HPjz608H32KmQMZYyCp/FJua/IrMslZk/O7D+qp1mpXZdb+kpXb8yu74Cdi8vW3h2lORcVPSVRNRIJNgYCXaBgVTpBDN3Teyw0ezLqU0u//GUKdfgXyr9PNI6RB3iJVWJVIcAbsX/EyxYZ/BYis+800z4FLzYvXJvc7Rnlc/DFhZUvZoqXrKdStp7teWL8uNeCpe02nKJlNun994ka2+pvN/kU6Zl7YzYM7PPwL+T+vXbVpJbV9Mn7pLeez18uSrENVFW5SdkYCHTrWaHJaVDHijipmEVnq0TXkPOPpIB/TGnnZAWSfOrBdF0iC1Jg1+WmzGVGGcboptsg+3FSwpCeqH3TWlV7ux0jPfeAiurWWY6ebROOLe/+OneWu8+Omz5lfJmMTGKncYfnPhf1vg4qx6wU3dDo6bkZpCUV13U5+FQlA9PuE+hyq6aKKnpYkSijeoo54QS32Wlq+eqC+hW+uGBoAarNrPwtMnHH8fG3Jm+XddosMdNsknDLVtLfXWZMXfuYwl4DlZWZhqbsmZUTEv9USlTX5bbhneuS5qgNbxc11j4qYVR4O0kaMS27e/Okk73HgePyvz6KU3LX5aydhTcstDZFbtP1Eeg2ioM+U+sadCNFts5G/Y7GNeW00IoCvve46bGAckrbRXdkzNzWwVPh9sOjy0RJdQ5MKBNXH6lzjso0F5n7L/Uyuhc9PzEqIu67KzVwKOpfb4n/r3tEdJwM3nwanrnxnaLrclLG33z1XLxS2uf0zuXJp3g9xgqdGhFlVsCNGRSFQLfe7b80XTkmsK9vBwSn/uDk1S2EL7jSfO0XPSMdfIl0wHkVqRIgSeo/0nucdV308+FAMngjtc+AwBMxF+fBLK1JZdMELpGJdPy8Bcf9S/7t83UNTAweUBOZ4Kt1WPb6VN9VIYFDFVp0g9PKtEXcDKrI9EWoC5MW5C+T9vtPml4oOBVj2tcKTgfV4Ah0613/lOOF+o+obD3QKXzCSZMdO5gJkpsS3dNJ381fZvvh0szP0+qPyjrr36Wzn5B6t0U/329w/LaZIDkseMzucXyeCqTourzrkd5j5LRw/nZtSa1RMfvuXaZp5lA5+y+VFvybtMcJ2ev3PTP/tknnzkFFdNMt57l4zxPjn0scS59i38F6Ln1MuvCp1NVChWTOo5nEopFSjtHt2Sv9dmkc8/XStu9CCHTr3f6frnUNUG5Hf63WNUAtDKZ7JOpE3/YCu+sW2JU0an2h4yEPudR7TOrdkBSEdIxdDKz77PMEAF1BjyZpt6Nzv99U481D2xx9Y+fv4Zs01c48W4kbmFHvYeieneOcUVtXvSmd+Zv459MeE61Ds5ebAj0FimnVb0oxNrxBEOjWux4pppYZnmc+XpRXzjx9Bf7zau5btqoAQNmMzSRQq0Cik+C84EnTC+2we3aZqAuyggKUQL1ah8a0EKNhhAOHYZPyb5N2iFhF+cf0gRfkPpU0xWDmhlU4EEJ96NkrkHk+SpFjdItJMthNe4oR6FZRxe4dRp0YUTnFnixO+Vii1AAAIABJREFUuVvae1G3PdmA7x11brKfiTV8jooLLHecErOjiPIHnJOuDgXNsZuim2rfqPlF0bCsiMvanQ9K2F+Vz9vNfTp/X/ywNGFe8njK6Z+TFq8igWFXVY7jK+tcWeSY3wbGPLqNYMLcWtege0t7oho/x/tB9xQ3HhKoF4Ve/PTbofP3fOfBrAzPSWN005xPU9Rz9vXeON/YYByNqQZTCIXtdkzh20TVc8S+0og7s9d9+tHshHBNPaUR+xX+eqgPn/h+/HNlv8nSPW+206LbFcy/M38ZAPUnM1ehFJFMAqhXxWSnDWyTL7hIdQFX4lQyPXsxv3wj2ud0ad63458fMrHwfSYej0UEB1NOLXybtK83bFJ8Qjh0HZmbFeE5vA+9PLBQ7By7MQZ2z/nCCXS7gqQ5A1F7SeNn0L1NPK7WNQDSy7QMTVlY2ddJ6p6cJgiOC6RJ+Nb4Pn6zNwdsnDn/FFqREASkae3dcXL28q5H59+mGJM+ITVvJ+11UmX2j/oUPt8FpyRqKXM+l6zx6gnH/ifvk+bfUd7XriG6LncFrUPinzvsqurVA9E+3n3StKNA4+d4Y7OTxoAB9aL/SOna93LXpwkIyt3NrtAxule8Rq8JhLLRppVw7B52dfbygedLz/+i+P3FaR8tXfm3wrdD15Tm/LbrUZWvR5TR02vzuhVCoNvVHfSZWtcAZDtEEsZlo9tJ2ZVu709KL/86tDJFkNDTD2aCiYcYAw+pwBsuKY7TxIy5JZh5TbpZNdB9pT2WixmH3o2SUdF1uUZmjZxV2Abn/j56fTeaCwsAUAND90xRqIhWrD2OT3gy4ULsuH+RPnYxU+uh005To9eX0huh1/bF1ydo8arcrqAHXywddGF59o+up99g7zGcJbzUAJRZPXLQoltFwcN3yuACM0EOGp+77vhbS6oPSrDr0Sm6LwFAAxiwszfFSe+EOWiD11elXKyluU5r21GadU3xr4HGc+pPpI3rCtsm6Tg95W5p8O5RG+XfbzjYICsywk77mdebpSzzenef1tliEOh2FWbS3Fuk+8/vXLfX/NrVp7sbOLrWNQCA6ln08+Tnh5Uwjc+AUZ2/999Z+vtfpOZAIpZT7yXTLJL16S/1iTgGM1mYZ12XsHHE3ZV8Q0569s5d16tN2vx+8naAJG2/U4nZuUvVfYJjAt0asWK6F+z9SWnALtLzD0qHf6H8lUJ63Wh8AwDk1SPQBW/QOO9x7i35tzvtZ9lTwsz7tvTySZ37kKSxM8tTR3Q/vfpFJ1iTpOY+3mMhQ8Ay//uj5mcesb/00ip11/lKUSPB69GB4+LLdVMEul3NLgd7PwAA1NpxK3IDhe2HS59/JzqRzw67ZS+PmZG93LtNmnBseesIRDn6a96wsDHF3EiJCGZ3nOIFum3DSq4aUJS530hXrhs11hDoAsWYME964p9rXQsAqK1Jn4heHxXkXvGq1MQUQKgTfdulGcsK26ZXP+8xqiv99CukCXNTJm8DIpQagDaXee7dBkDW5RoxurZ0bSP2lfoOlPY9s9Y1AYCuoff2UnPE2Eagqxg2STrpu9IxN+Y+16OJIBfVt/cnAwspA+VMt/1ugBZdoFiXvVzrGgAAgGqiaz0qpc+AwrcpZg7xth07fz/yK4Vv34UQ6AIAAABALW03UDrw/OQy5Z4/PNMdv0ER6NZIUVmXAQAAADSm2dcnPz/ygPjnihrj29jxCGN0AQAAAKBejfJnXKGhrCAEugAAAABQr8YcVusadEkEugAAAABQ95JadIvoutzgLcQEugAAAADQSGZd5z22j6ltPWqIQLeKSpwGGgAAAEB3k5kSaPvh6bcZf4T32KOp/PXpIsi6XCPW4FnOAAAAAJTBXp/w5tkde3h8mR12i3kiKeZo7HiEQLeKHG26AAAAAAphJo2fE/3cCbdJrcOk5j6F77e5d2n1qnMEujUyd8zcWlcBAAAAQFe254nR63v4YV6f/rnPLXlEeuKfpd0+Xrl61QEC3Rrp19Kv1lUAAAAA0IgGjZWOWC5NPD73ueFTpfl3VL1K1UagCwAAAACNZtrZta5BTZWUddnM2s1slZm96D8OiCm3yC/zopkt8tf1NbNfmNlzZvaMmS0PlD/dzN4yszX+z5JS6lkvGKELAAAAAJVX6vRCV0h6xDk3TtIj/nIWM2uXdI2k/SXtJ+maQED8VefcbpKmSDrIzI4MbPoj59xk/+fWEusJAAAAAOgmSg10j5V0p//7nZLmRZSZI2mVc26Dc26jpFWSjnDO/cM592tJcs5tkfQnSQVMDtX10KILAAAAAJVXaqA7xDn3uiT5j4Mjyuwk6bXA8np/XQcz6y/p4/JahTNOMLO1ZnaPmY2Iq4CZnWVmq81s9VtvvVXs+wAAAAAANIi8ga6ZPWxmT0f8HJvyNaJmIu5o3DSznpJ+IOkbzrmX/dU/lzTKObeXpIfV2WqcuyPnVjjnpjrnpu6www4pqwQAAAAAaFR5sy4752bFPWdmb5jZMOfc62Y2TNKbEcXWS5oeWB4u6TeB5RWSXnTOfT3wmu8Env+OpBvy1RMAAAAAAKn0rsv3S1rk/75I0n0RZVZKmm1mA/wkVLP9dTKz6yVtL+kzwQ38oDljrqRnS6wnAAAAAKCbKDXQXS7pcDN7UdLh/rLMbKqZ3SpJzrkNkr4o6Q/+zxeccxvMbLikKyVNkPSn0DRCF/hTDj0l6QJJp5dYTwAAAABAN5G363ISv4vxzIj1qyUtCSzfLun2UJn1ih6/K+fcMknLSqlbPSLrMgAAAABUXqktugAAAAAA1BUCXQAAAABAQyHQBQAAAAA0FAJdAAAAAEBDIdAFAAAAADQUAt0qIusyAAAAAFQegW4VEegCAAAAQOUR6AIAAAAAGgqBLgAAAACgoRDoAgAAAAAaCoEuAAAAAKChEOhWEcmoAAAAAKDyCHSriEAXAAAAACqPQBcAAAAA0FAIdAEAAAAADYVAFwAAAADQUAh0AQAAAAANhUAXAAAAANBQeta6At2Js1rXAAAAAEClbd68WRs2bNCmTZu0bdu2WlenrjU1Nam1tVXt7e3q1atX2fZLoAsAAAAAZbJ582a9+uqrGjBggEaNGqXm5maZ0eIVxTmnDz/8UO+//75effVVjRw5smzBLl2XAQAAAKBMNmzYoAEDBmjQoEFqaWkhyE1gZmppadGgQYM0YMAAbdiwoWz7JtAFAAAAgDLZtGmT2traal2NLqetrU2bNm0q2/4IdAEAAACgTLZt26bm5uZaV6PLaW5uLut4ZgLdKnK1rgAAAACAiqO7cuHK/ZkR6FYRgS4AAAAAVB6BLgAAAACgoRDoAgAAAAAaCoEuAAAAAKChEOgCAAAAABoKgW4VkYwKAAAAACqPQBcAAAAAUJT/+Z//0fDhwzVy5Eht3rw567klS5aoqalJP/zhD6teLwLdKnK06QIAAABoIH369NF1112n1157Td/61rc61i9btky33XabbrnlFi1YsKDq9epZ9VcEAAAAgG7mup8/o//42/u1rkaWCTu26ZqPTyx5P6effrpuuukmffnLX9aZZ56pW2+9VcuXL9d1112nc845pww1LRwtugAAAACAojU1NWn58uV66623NG/ePF188cU6//zzdfXVV9esTrToAgAAAECFlaPltJ4dc8wx2nvvvfXII49owYIFuvnmm2taH1p0AQAAAAAlufvuu7VmzRpJUmtrq8yspvUh0AUAAAAAFO2hhx7SaaedpuOOO04LFizQ7bffrmeffbamdSLQrSJyLgMAAABoJE8++aSOP/54HXTQQbrrrrt0/fXXq0ePHlq2bFlN60WgCwAAAAAo2LPPPqujjz5a48eP189+9jP16tVLY8aM0eLFi3Xffffp8ccfr1ndCHQBAAAAAAV59dVXNXv2bG2//fZ68MEH1dbW1vHc1VdfrT59+uiyyy6rWf3IugwAAAAAKMjIkSP12muvRT43bNgw/eMf/6hyjbLRoltFjNEFAAAAgMoj0AUAAAAANBQCXQAAAABAQyHQrSK6LgMAAABA5RHoAgAAAAAaCoEuAAAAAKChEOhWUVPm0dGJGQAAAAAqhUC3itqc93H/77+/WeOaAAAAAEDjItCtgYHbPqp1FQAAAACgYRHoVhEdlgEAAACg8gh0a8BqXQEAAAAAaGAlBbpm1m5mq8zsRf9xQEy5RX6ZF81sUWD9b8zseTNb4/8M9tf3MrMfmdlLZvakmY0qpZ4AAAAAgO6j1BbdKyQ94pwbJ+kRfzmLmbVLukbS/pL2k3RNKCBe6Jyb7P9ksjQtlrTROTdW0k2SbiixngAAAACAbqLUQPdYSXf6v98paV5EmTmSVjnnNjjnNkpaJemIAvZ7j6SZZkaPXwAAAABAXqUGukOcc69Lkv84OKLMTpJeCyyv99dl/KvfbfnzgWC2Yxvn3FZJ70kaGFUBMzvLzFab2eq33nqrtHcDAAAAAOjyeuYrYGYPSxoa8dSVKV8jqiU2k4B4oXPuP82sVdJPJJ0m6bt5tsle6dwKSSskaerUqSQ2BgAAAIBuLm+LrnNulnNuj4if+yS9YWbDJMl/fDNiF+sljQgsD5f0N3/f/+k/bpL0b/LG8GZtY2Y9JW0vaUMxb7CeOCYYAgAAANBAli5dKjPT3/72t5znnn/+ebW0tOjCCy+ser1K7bp8v6RMFuVFku6LKLNS0mwzG+AnoZotaaWZ9TSzQZJkZs2SjpH0dMR+T5T0K+dcw0SJRsALAAAAoAEccMABkqTf//73Oc9ddNFFamtr07XXXlvlWqXoupzHckl3m9liSa9Kmi9JZjZV0lLn3BLn3AYz+6KkP/jbfMFft528gLdZUpOkhyV9xy9zm6TvmdlL8lpyF5RYTwAAAAConQevkP7+l1rXItvQPaUjl5e0i2nTpknyAt158zpzE//iF7/Qgw8+qG9+85saMCByFtqKKinQdc69I2lmxPrVkpYElm+XdHuozH9L2idmvx/ID5oBAAAAAPVp1113VXt7e1aL7ocffqiLL75Ye+yxhz796U/XpF6ltugCAAAAAPIpseW0nk2bNk2PP/64nHMyM91888164YUX9PDDD6upqakmdSp1jC4AAAAAoBubNm2a3nvvPT3//PN688039cUvflHz5s3TzJk5nX+rhhbdKurnz5rURC4qAAAAAA0imJDq0Ucf1ebNm/W1r32tpnUi0K2im7a06sH/Xqedt26tdVUAAAAAoCz2339/9ejRQ7fddpsee+wxXXrppRo9enRN60TX5SoarB5a9P4mv10XAAAAALq+1tZWTZgwQY8++qgGDx6sK6+8stZVItAFAAAAAJRmv/32kyR9+ctfVmtra41rQ6ALAAAAACjBhx9+qN/85jeaOnWqFi1aVOvqSGKMLgAAAACgBF/96le1bt063XXXXTKrj4GaBLoAAAAAgIJs2LBBK1eu1Nq1a/WVr3xFF198saZNm1branUg0AUAAAAAFGTlypU65ZRTNHjwYF100UVavnx5rauUhUAXAAAAAFCQk08+WSeffHKtqxGLZFQAAAAAgIZCoAsAAAAAaCgEugAAAACAhkKgCwAAAABoKAS6AAAAAICGQqALAAAAAGgoBLoAAAAAgIZCoAsAAAAAaCgEugAAAACAhkKgCwAAAABoKAS6AAAAAICGQqALAAAAAGgoBLoAAAAAgIZCoAsAAAAAaCgEugAAAACAok2dOlW77757x/IJJ5yg9vb2juXzzjtPZqZ33323anUi0AUAAAAAFGXbtm165plnNHny5I51a9as0aRJk7KWR40apf79+1etXj2r9koAAAAA0E3d8Psb9NyG52pdjSy7te+my/e7vKR9PPfcc/rggw86At1NmzZp3bp1mjt3riTJOae1a9dq1qxZJde3ELToAgAAAACKsmbNGknqCHTXrFkj51zH8ssvv6xNmzZltfhWAy26AAAAAFBhpbac1qunnnpKUnagK0lTpkzJWq52oEuLLgAAAACgKE899ZSGDh2qIUOGSPIC25aWlo7kVJlAOBP4VguBLgAAAACgKC+88IJ23HHHjuU1a9Zo4sSJam5uliQ9+OCDGjFihEaMGFHVehHoAgAAAACKsnXrVr3zzjtyzmnr1q1ZGZgfeughrV69WgsXLqx6vQh0AQAAAABFmTFjhl555RUtXbpUP/3pT7V582b1799fN954o+bPn6/x48dr2bJlVa8XyagAAAAAAEW58cYb9cYbb2jFihVasWKFJOmmm25S7969tWTJEl177bVqa2urer1o0QUAAAAAFGXQoEFauXKlXnrpJc2ZM0dmplWrVmnjxo265ZZbNHDgwJrUixZdAAAAAEBJxowZoy1btmj06NGaNWtWratDiy4AAAAAoHRr166t+ny5cQh0AQAAAAAlWb9+vd555526CXTpugwAAAAAKMnw4cPlnKt1NTrQogsAAAAAaCgEugAAAACAhkKgCwAAAABoKAS6AAAAAICGQqALAAAAAGgoBLoAAAAAgIZCoAsAAAAAaCgEugAAAACAhkKgCwAAAABoKAS6AAAAAICGQqALAAAAAGgoBLoAAAAAgIZSUqBrZu1mtsrMXvQfB8SUW+SXedHMFvnrWs1sTeDnbTP7uv/c6Wb2VuC5JaXUEwAAAADQfZTaonuFpEecc+MkPeIvZzGzdknXSNpf0n6SrjGzAc65Tc65yZkfSa9Iujew6Y8Cz99aYj0BAAAAAN1EqYHusZLu9H+/U9K8iDJzJK1yzm1wzm2UtErSEcECZjZO0mBJvy2xPgAAAACAKjnjjDNkZnrrrbdynnvppZfU0tKis88+u+r1KjXQHeKce12S/MfBEWV2kvRaYHm9vy7oZHktuC6w7gQzW2tm95jZiBLrCQAAAAAos7322kuS9PTTT+c8d/nll6t379667rrrql0t9cxXwMweljQ04qkrU76GRaxzoeUFkk4LLP9c0g+cc5vNbKm81uLDYup3lqSzJGnkyJEpqwQAAAAA1fP3L31Jm599rtbVyNJr99009HOfK2kfwUB3xowZHet/97vf6d5779WXvvQlDR4c1R5aWXkDXefcrLjnzOwNMxvmnHvdzIZJejOi2HpJ0wPLwyX9JrCPSZJ6Ouf+GHjNdwLlvyPphoT6rZC0QpKmTp0aDqABAAAAABUyadIkSbktupdccolGjBihiy66qBbVyh/o5nG/pEWSlvuP90WUWSnpS4GMzLMlLQs8f7KkHwQ3yATP/uJcSc+WWE8AAAAAqJlSW07r1cCBAzVs2DA988wzHevuvvtuPfHEE/r+97+v3r1716RepQa6yyXdbWaLJb0qab4kmdlUSUudc0uccxvM7IuS/uBv8wXn3IbAPk6SdFRovxeY2VxJWyVtkHR6ifUEAAAAAFTAXnvtpSeffFKStGXLFi1btkz77ruvTjnllJrVqaRA1+9iPDNi/WpJSwLLt0u6PWYfoyPWLVN2qy8AAAAAoA7ttddeWrlypdavX68f//jHevnll3XHHXfILCpdU3WUmnUZAAAAANCNZRJS/fa3v9X111+v448/XgcffHBN60SgCwAAAAAoWibQ/cxnPqNNmzbphhticwlXDYEuAAAAAKBou+++u1paWvTmm2/q3HPP1dixY2tdpZKTUQEAAAAAurHm5mZt3ry51tXIQosuAAAAAKChEOgCAAAAABoKgS4AAAAAoKEQ6AIAAAAAGgqBLgAAAACgoRDoAgAAAAAaCoEuAAAAAKChEOgCAAAAABoKgS4AAAAAoKEQ6AIAAAAAGgqBLgAAAACgoRDoAgAAAAAaCoEuAAAAAKBsPvroI33wwQfatm1bzepAoAsAAAAAKJtHH31Uffr00fe+972a1YFAFwAAAADQUHrWugIAAAAAgMYxffp0OedqWgdadAEAAAAADYVAFwAAAADQUAh0AQAAAAANhUAXAAAAAFC0qVOnavfdd+9YPuGEE9Te3t6xfN5558nM9O6771atTgS6AAAAAICibNu2Tc8884wmT57csW7NmjWaNGlS1vKoUaPUv3//qtWLQBcAAAAAUJTnnntOH3zwQUegu2nTJq1bt65j2TmntWvXasqUKVWtF9MLAQAAAECF/fbuF/T2a/9V62pkGTSinw4+aXxJ+1izZo0kdQS2a9askXOuY/nll1/Wpk2bslp8q4EWXQAAAABAUZ566ilJ2YGupI4W3HAgXC206AIAAABAhZXaclqvnnrqKQ0dOlRDhgyR5AW2LS0tHcmpMoFwtbsu06ILAAAAACjKCy+8oB133LFjec2aNZo4caKam5slSQ8++KBGjBihESNGVLVeBLoAAAAAgKJs3bpV77zzjpxz2rp1a1YG5oceekirV6/WwoULq14vAl0AAAAAQFFmzJihV155RUuXLtVPf/pTbd68Wf3799eNN96o+fPna/z48Vq2bFnV68UYXQAAAABAUW688Ua98cYbWrFihVasWCFJuummm9S7d28tWbJE1157rdra2qpeL1p0AQAAAABFGTRokFauXKmXXnpJc+bMkZlp1apV2rhxo2655RYNHDiwJvWiRRcAAAAAUJIxY8Zoy5YtGj16tGbNmlXr6tCiCwAAAAAo3dq1a6s+X24cAl0AAAAAQEnWr1+vd955p24CXbouAwAAAABKMnz4cDnnal2NDrToAgAAAAAaCoEuAAAAAKChEOgCAAAAABoKgS4AAAAAoKEQ6AIAAAAAGgqBLgAAAACUUT1lH+4qyv2ZEegCAAAAQJk0NTXpww8/rHU1upwPP/xQTU1NZdsfgS4AAAAAlElra6vef//9Wlejy3n//ffV2tpatv0R6AIAAABAmbS3t2vjxo16++23tWXLFroxJ3DOacuWLXr77be1ceNGtbe3l23fPcu2JwAAAADo5nr16qWRI0dqw4YN+utf/6pt27bVukp1rampSa2trRo5cqR69epVtv0S6AIAAABAGfXq1UvDhg3TsGHDal2VbouuywAAAACAhkKgCwAAAABoKAS6AAAAAICGUlKga2btZrbKzF70HwfElPulmb1rZg+E1u9iZk/62//IzFr89b385Zf850eVUk8AAAAAQPdRaovuFZIecc6Nk/SIvxzlK5JOi1h/g6Sb/O03Slrsr18saaNzbqykm/xyAAAAAADkVWqge6ykO/3f75Q0L6qQc+4RSZuC68zMJB0m6Z6I7YP7vUfSTL88AAAAAACJSg10hzjnXpck/3FwAdsOlPSuc26rv7xe0k7+7ztJes3f71ZJ7/nlAQAAAABIlHceXTN7WNLQiKeuLPG1o1poXYrnsndidpaks/zF/zKz50usV6UNkvS2rqOBGmXlHVdAeXFcoVI4tlAJHFeoBI6r+rNzmkJ5A13n3Ky458zsDTMb5px73cyGSXqzgAq+Lam/mfX0W22HS/qb/9x6SSMkrTeznpK2l7Qhpn4rJK0o4HVrysxWO+em1roeaCwcV6gEjitUCscWKoHjCpXAcdV1ldp1+X5Ji/zfF0m6L+2Gzjkn6deSTozYPrjfEyX9yi8PAAAAAECiUgPd5ZION7MXJR3uL8vMpprZrZlCZvZbST+Wl1RqvZnN8Z+6XNLFZvaSvDG4t/nrb5M00F9/seKzOQMAAAAAkCVv1+Ukzrl3JM2MWL9a0pLA8sEx278sab+I9R9Iml9K3epYl+lmjS6F4wqVwHGFSuHYQiVwXKESOK66KKNHMAAAAACgkZTadRkAAAAAgLpCoFslZnaEmT1vZi+ZGWOOkcjMRpjZr83sWTN7xswu9Ne3m9kqM3vRfxzgrzcz+4Z/fK01s70D+1rkl3/RzBbFvSa6DzNrMrM/m9kD/vIuZvakf4z8yMxa/PW9/OWX/OdHBfaxzF//fCDvAroxM+tvZveY2XP+uesAzlkolZld5P8ffNrMfmBmvTlnoRhmdruZvWlmTwfWle0cZWb7mNlf/G2+YWbMJVpjBLpVYGZNkr4p6UhJEySdbGYTalsr1Lmtkj7rnNtd0jRJ5/rHzBWSHnHOjZP0iDoTtR0paZz/c5akb0veCVzSNZL2lzce/prMSRzd2oWSng0s3yDpJv+42ihpsb9+saSNzrmxkm7yy8k/FhdImijpCEnf8s9z6N5ulvRL59xukibJO8Y4Z6FoZraTpAskTXXO7SGpSd65h3MWinGHvO8/qJznqG/7ZTPbhV8LVUagWx37SXrJOfeyc26LpB9KOrbGdUIdc8697pz7k//7JnkXjDvJO27u9IvdKWme//uxkr7rPP9X3hzVwyTNkbTKObfBObdR0ipx4u3WzGy4pKMl3eovm6TDJN3jFwkfV5nj7R55mfPNX/9D59xm59w6SS8pIrEgug8za5N0iPzZE5xzW5xz74pzFkrXU1IfM+spqa+k18U5C0Vwzj0qaUNodVnOUf5zbc65J/wpUb8b2BdqhEC3OnaS9Fpgeb2/DsjL73o1RdKTkoY4516XvGBY0mC/WNwxxrGHsK9LukzSR/7yQEnvOue2+svBY6Tj+PGff88vz3GFsNGS3pL0r363+FvNbDtxzkIJnHP/Kemrkl6VF+C+J+mP4pyF8inXOWon//fwetQQgW51RPXRJ9018jKzfpJ+Iukzzrn3k4pGrHMJ69ENmdkxkt50zv0xuDqiqMvzHMcVwnpK2lvSt51zUyT9tzq7AEbh2EJefpfQYyXtImlHSdvJ61IaxjkL5VboscQxVocIdKtjvaQRgeXhkv5Wo7qgizCzZnlB7l3OuXv91W/43WPkP77pr487xjj2EHSQpLlm9ld5QygOk9fC29/vFihlHyMdx4///Pbyun1xXCFsvaT1zrkn/eV75AW+nLNQilmS1jnn3nLOfSjpXkkHinMWyqdc56j1/u/h9aghAt3q+IOkcX6WwBZ5CRHur3GdUMf8MUW3SXrWOXdj4Kn7JWUy/C2SdF9g/Sf9LIHTJL3nd8FZKWmtZljaAAAENklEQVS2mQ3w74zP9tehG3LOLXPODXfOjZJ3HvqVc26hpF9LOtEvFj6uMsfbiX55569f4Gc43UVe0o3fV+ltoA455/4u6TUz29VfNVPSf4hzFkrzqqRpZtbX/7+YOa44Z6FcynKO8p/bZGbT/GP1k4F9oUZ65i+CUjnntprZefL+OJok3e6ce6bG1UJ9O0jSaZL+YmZr/HWfk7Rc0t1mtljeBcB8/7n/I+koeQk2/iHpU5LknNtgZl+Ud7NFkr7gnAsnYgAul/RDM7te0p/lJxTyH79nZi/JaxVZIEnOuWfM7G55F5xbJZ3rnNtW/Wqjzpwv6S7/hu7L8s5DPcQ5C0Vyzj1pZvdI+pO8c82fJa2Q9AtxzkKBzOwHkqZLGmRm6+VlTy7nddXZ8jI795H0oP+DGjLvRhcAAAAAAI2BrssAAAAAgIZCoAsAAAAAaCgEugAAAACAhkKgCwAAAABoKAS6AAAAAICGQqALAECFmNk2M1sT+BllZlPN7Bv+86eb2T/7v88zswklvl5fM7vLzP5iZk+b2WNm1s/M+pvZOeV4TwAAdAXMowsAQOX8j3NucmjdXyWtjig7T9ID8ub6TMXMejrntgZWXSjpDefcnv7zu0r6UNIgSedI+lb6qgMA0HXRogsAQBWZ2XQzeyC07kBJcyV9xW/5HeP//NLM/mhmvzWz3fyyd5jZjWb2a0k3hHY/TNJ/Zhacc8875zZLWi5pjL/vr/j7udTM/mBma83sOn/dKDN7zszu9NffY2Z9K/ZhAABQIbToAgBQOX3MbI3/+zrn3HFRhZxzvzOz+yU94Jy7R5LM7BFJS51zL5rZ/vJaYw/zNxkvaZZzbltoV7dLesjMTpT0iKQ7nXMvSrpC0h6Z1mUzmy1pnKT9JJmk+83sEEmvStpV0mLn3ONmdru8luCvlv5RAABQPQS6AABUTlTX5bzMrJ+kAyX92Mwyq3sFivw4IsiVc26NmY2WNFvSLEl/MLMDJP1PqOhs/+fP/nI/eYHvq5Jec8497q//vqQLRKALAOhiCHQBAKg/PSS9mxAk/3fchs65/5J0r6R7zewjSUdJ+kmomEn6snPuX7JWmo2S5MK7TF9tAADqA2N0AQCoD5sktUqSc+59SevMbL4kmWdSvh2Y2UFmNsD/vUXSBEmvBPftWynpDL/lWGa2k5kN9p8b6bcCS9LJkh4r+Z0BAFBlBLoAANSHH0q61Mz+bGZjJC2UtNjMnpL0jKRjU+xjjKR/N7O/yOuWvFrST5xz70h63J9y6CvOuYck/ZukJ/yy96gzEH5W0iIzWyupXdK3y/geAQCoCnOOHkkAAKCj6/IDzrk9alwVAABKQosuAAAAAKCh0KILAAAAAGgotOgCAAAAABoKgS4AAAAAoKEQ6AIAAAAAGgqBLgAAAACgoRDoAgAAAAAaCoEuAAAAAKCh/H9QL2nvmeugzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,9))\n",
    "plt.step(range(len(measurements[0])),Kx, label='$x$')\n",
    "plt.step(range(len(measurements[0])),Ky, label='$y$')\n",
    "plt.step(range(len(measurements[0])),Kdx, label='$\\psi$')\n",
    "plt.step(range(len(measurements[0])),Kdy, label='$v$')\n",
    "plt.step(range(len(measurements[0])),Kddx, label='$\\dot \\psi$')\n",
    "\n",
    "\n",
    "plt.xlabel('Filter Step')\n",
    "plt.ylabel('')\n",
    "plt.title('Kalman Gain (the lower, the more the measurement fullfill the prediction)')\n",
    "plt.legend(prop={'size':18})\n",
    "plt.ylim([-0.1,0.1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## State Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plotx():\n",
    "    fig = plt.figure(figsize=(16,16))\n",
    "\n",
    "    plt.subplot(411)\n",
    "    plt.step(range(len(measurements[0])),x0-mx[0], label='$x$')\n",
    "    plt.step(range(len(measurements[0])),x1-my[0], label='$y$')\n",
    "\n",
    "    plt.title('Extended Kalman Filter State Estimates (State Vector $x$)')\n",
    "    plt.legend(loc='best',prop={'size':22})\n",
    "    plt.ylabel('Position (relative to start) [m]')\n",
    "\n",
    "    plt.subplot(412)\n",
    "    plt.step(range(len(measurements[0])),x2, label='$\\psi$')\n",
    "    plt.step(range(len(measurements[0])),(course/180.0*np.pi+np.pi)%(2.0*np.pi) - np.pi, label='$\\psi$ (from GPS as reference)')\n",
    "    plt.ylabel('Course')\n",
    "    plt.legend(loc='best',prop={'size':16})\n",
    "\n",
    "    plt.subplot(413)\n",
    "    plt.step(range(len(measurements[0])),x3, label='$v$')\n",
    "    plt.step(range(len(measurements[0])),speed/3.6, label='$v$ (from GPS as reference)')\n",
    "    plt.ylabel('Velocity')\n",
    "    plt.ylim([0, 30])\n",
    "    plt.legend(loc='best',prop={'size':16})\n",
    "\n",
    "    plt.subplot(414)\n",
    "    plt.step(range(len(measurements[0])),x4, label='$\\dot \\psi$')\n",
    "    plt.step(range(len(measurements[0])),yawrate/180.0*np.pi, label='$\\dot \\psi$ (from IMU as reference)')\n",
    "    plt.ylabel('Yaw Rate')\n",
    "    plt.ylim([-0.6, 0.6])\n",
    "    plt.legend(loc='best',prop={'size':16})\n",
    "    plt.xlabel('Filter Step')\n",
    "\n",
    "    plt.savefig('Extended-Kalman-Filter-CTRV-State-Estimates.png', dpi=72, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ccMbUFvtiVQIA3/3xfADmb7ejehZss9tLurfkoi52Dsef96SyeKdtpb2sdyvG9bKLFSzZlcIP62135jE9WjCsYxhgx+qu2ZcGwPCO4QxqFwrAnC2HKCoypGXnER3qy5D2tjzWqftNrJ0n8utJQwBYvTfNFe/nKxNc+6W7UE/5bhPfxCZy1+drXd2kAdbsS3Ml7kopdSZKS0vD29ubqKgoAgICcHd310S3nogIXl5ehIWFERISQlpa2qlPOk3a30gppZRqAEVFhiv+tZyc/JLucN3+NqtMnRXOrMlRIX70bxuCMZCZk888J9kd3TWCy/vYpPanDQddyW6vyCD6RgUDMGfLYX7eY68zrEMYPp7u+Hm5M397sisJntCrFe5Oq3JC2nHWJR4F4NLuLfH2sPU3JNpW4xV7bJfpri0DaR3kw/ZDtiU3yZkka3C7UPy93NntjAs2xriSdICN++2Mz4t3pnDtOysY9Oz82ryNSilVrzIyMmjevLkmuA0sMDCQrKysOr+uJrtKKaVUA3hlnp306Zr+Uex9bryrPC07D4CsnHwKiwxD2zcH4Mq+rQGb1G7YbxPPEH8vOkbY8bIr9hxh28FMArw9EBHc3IRm3h7sSj7G+sR0mnl74OVh/5tvH+4PwAxn0qqekYEADO8YxtHj+Uxda5Pg4i7HfaKCyc4rJL+wiCPH8uja0r5m99ZBrpi/XbsfgN+eH8OITnZyyMycfFe35ogA2xVt2W6bLL+1cLfrnjc69zNv62FG/XMRU51rKaVUYysoKMDLy6uxwzjneHp6UlhYeOqKp0mTXaWUUqoBvLHAJnvP/qonIsJjE7rZ59O3AfDDervYwMXdWwAw3umW/P26/aRk5dK9VaDrWu3C/DmQYbsDn9+xuav8wi4lKxIM7VBSPrGPXaI+40Q+rYJ8XC0WfaNta/BXq2135P5tQwDo45S/Ns8uQXRh5/Ay11+xJ5W5zhjf0d0iGBBjz1sVl8Y8p7x4XO9SZ0KsVaW6Py9xWqQfn7aFuCPZPDdjm+tYfmERGcfzK34TlVKqAWirbsOrr/dck12llFKqFvYfPc5lbywl5pHpfLMmocI6O53Wzi4tAvD2cAfgjuHtAJi52S4yMH2j3V7dzyamzZvZltE1+2wX48HOWFqAS3uUTPp0w8A2rv37RnVy7d89sqNr/7qBJav43Xp+jGv/+lLl0aG+ri8bl/awCfe/nNbYq/pFAXCRk/Qu253CpqQM3N0Ebw931zjiVXGpLNlpW3JHdbVle1Kyycm3f60/r41Non9xZpYu7gqdmp3nGtvb76m59HlqDolpx1FKKaVqQ5NdpZRSqoYOZ+Yw/IWFbE7KBODhbzcx+btN5er9Y9YOAB67rJurTES4cVA0x/MKWb77CCviUhHBNZMy2LG1xYqTY4AHLunMoHah3DAwmpFOUgnQpWUA3/3xfH64e5ir1RYgyNeT/909jGd/1ZNJF7R3lUeH+rlacx8d391VXvrc4usW1wf4arWdtGq089rtw2w36XWJ6axLPIoIeHm40TsqiJSsXGKdhH1Md5ukH8rIISG1bDK75UAmSeknyMotAMpOeJWclUNuQd13b1NKKdW0abKrlFJK1dDolxYD8Or1fXn6Sttt96vVCa5xuMXmbbNde4c7MyMXu9dpib3pg1X2eqUSV4Dnr+7F2B4teXBMZ6JC/FzlXh5u/OfOoTx/de9yMfVrE+Lqhlxa3+hgbhrctlxXsf/eOZTtT48ts0SQiPDnSzrj5eHGny7uXKZ+6yAf1/6vB9tWZTc3IcjXk7XxR8kvNAzrYO+zhzPG92unxXtEpzC6tAhg68FMljsTX13ltGTvOJTFbGfpJSh5z+ZsOcSgZ+cz8V/Ly92TUkopVRVNdpVSSqka2LQ/g2O5BYQ18+bK8yK5eUhb/m+0TV77PT3XVW/rAdvqO6hdaLlEs3WwL/3alCSmj03oXuZ4gI8n79zcn3tKdU+ua25ugo+ne7nye0d3Yucz4/i/i8u+9rO/6gWAt4ebaywvwACnhRjsOF6APlE22f3J6aLdo3Ug7ZxW4OIljG4fZlusNyVlsHBHySzO2U4L71uL9gCw/VBWmWWMlFJKqVPRZFcppZSqgSnf2+7Kb/76PFfZny4paQUtTnIf+5+t95dLu1R4nS9+N4SHx3blf3cPI8ZJBM9kI7tGsObRi9n85KVlkvdbh8W49ovHEQ9pXzJJVniANyLianX+fp3tplw88VZS+gn2pWYDcFGXcOKO2P3i8c4Ae52ytxbtpvOjM3XNXqWUUlXyON0TROSqalTLMcbMqEE8SimlVKP7+4xtvLckDoANfxtDkJ9nmePGGDYl2fVjB5dK6AA+uW0gt368hvGvL2X3s+NckzENiAmlIr5e7vzhog51fQv1KtxZVqi0EZ3CmffAhQT6euDrZVuKSyfvxa3AvZ3WXoDIYF/c3IQAHw/WJ6aTkpVL76gggn3t+51xIp/jeSVjdWP3HaVtc3/XGOgvVyVw/0ndrJVSSqliNWnZfR+4DLi8iscbdRWgUkop1ZA+WBrnSnQB+jw1h8Kist1nf3S65V7TP6rc+Rd1iSDU304y1fHRmQA8VEmrblPTMaIZEQE+Zcrev2UAdwxvx1MTewAwsFTSXzyGuV2YPylZuQB0ighwtQj/tNEux3TjINtSvCs5y7WUEcByZw1fYwwfL99L7L6S5Y2UUkqpmiS7M40xtxtjbqvsAayo60CVUkqp+paYdpxnpm8j2M+TjU+McZWPfXVJmXpvOmvm/mVsxUnswj9fRJcWdgbjbq0C+cOFZ1fLbV26pHsL/npZd/y8bGcyLw83OoTbFt/i5ZH6tSkZ7zuqawStg30BePLHrQCM72Unz9p5+JhrZmewyxoBfL4ynid/3Mo17+jXD6WUaghTpkxBRLjkkkvKHTPGcNNNNyEijB8/nvz8xls7/bSTXWPMb+qijlJKKXWmue/rdQA8f1VvAn082fP38QDsSj7GnpRjAOQVFLHjcBZe7m7lWjGLBfl5Muv+EaycPJrp9w7HzU0qrHeumv/ni9j3/ATaNLczTI/oVDJL9cXdI1ytv3kFRYBtDfbzcich7TirndbbAW1DXLNeL3bW9i19TlGR4ZgzyZVSSqm6NXnyZCIiIpg3bx7z5s0rc+zee+/lyy+/ZMSIEXz77bd4enpWcpX6V+MJqkTEXUSuEJH7ROSB4kddBqeUUko1FGMM65zxtcXL8Li7Cf9wlve5zmk1/Gj5XgBuHBRd5fVEhJZBPproVsOorhG8eE1vPrt9EN4e7q4xvwD+Xu74eLoTHeLH3iPZrN6bRnN/LzqENwMgMyfftUwRwBonGb76nZ/p+fhsdidnoZRSqm4FBATw+OOPAzbxLfa3v/2NN998k/79+/PTTz/h6+vbWCECNZigqpQfgRxgE1BUN+EopZRSjWPq2v0AXD+gbBJ73cBo/vLtRlKz81iw/TBvLrRdmB8a27XBY2yqRIRrT3rfB7QNITb+KOc743q7tQpghzMzc/twf7q2st3EtyTZWa+b+3uRmp1HXMox+rcNcf3h4vt1STx0qX5WSqm68eSPW1yz7Z8turcO5PHLe9T5dSdNmsQbb7xBbGwsU6dOJSkpiaeffppu3boxa9YsAgMD6/w1T1dtkt0oY0z51eyVUkqpM5AxhncWx/HCrO08Mq4rd500jvbj5fuAisfh/nD3MCa+uZzbP4kFYGLf1jTzrs1/oepU3r25Pyvj0krW7I0O5n/r7YRVl/ZoSacIm+y+tcj+8eHmoW15dd4uNidlEupfsl7vz3tSAcjJL+T9JXEM6xRWZoywUkqpmvHw8OCFF15g4sSJ/OEPfyA1NZWYmBjmzp1LWFjYqS/QAGrzP/VMERljjJlTZ9EopZRS9eSeL9cxfZOdRfn5mdvZdjCT126wa+QWFRm2HszEy8ON5s3KL6vTJzqYL343mNfm7SI80JuXru3ToLGfi5o382ZC71au52N6tHRNWHVVvyg83G338KW77HjdcT1b8eq8Xew9kl2m63icM4nVZyv28dLcnbw0dyf7np/QQHehlGpq6qOF9Gx2xRVX0KNHD7Zs2eIawxsZGdnYYbnUJtldCXwvIm5APiCAMcY0fnu1UkopVcqiHcmuRLfYD+sP8MLVvfHxdGfhDtsSeHW/yv+DHtYxjGEdz4y/VJ+LIoN9+fi2gWBwLe1UWucWzYgM9mVvaja5hXZ01dD2zVkRZ1t2520rae0tLDK461hqpZSqtddff50tW7YAkJOTc0Z0XS6txhNUAS8BQwE/Y0ygMSZAE12llFJnols/XoObwMYnxrDv+Qn4etoJkG54byUAL8zaDsDvRrRvtBjVqY3sEsHIrhGu5+3C7BJG53dojogQEehNSlYuKZk5RIX4EhNmZ3vOzMln75Fs13mbkjIAmPRZLL2emO1a41cppVT1ffrpp9x///1ERkZy+eWXk5mZyZNPPtnYYZVRm2R3F7DZGGPqKhillFKqrs3abFt0z+8QRqCPXf5g85OXArA+MZ2c/EJ2HrbLChXP8KvODi9c3ZuLuoTzx4vser2dIuzndyAjh4u7taBbK/s3+LX7jpKSleta+3hdwlHyCoqYs/UwWTkF/HdtYuPcgFJKnaW+//577rjjDkJDQ5k7dy5vvvkmPj4+vPvuu+zcubOxw3OpTbJ7EFgkIpNPZ+khEdknIptEZL2IxDploSIyV0R2OdsQp1xE5HUR2S0iG0WkXy3iVUopdQ56Y4GdwOjZX/V0lbm7CZc540G7/nUWAHeP7FD+ZHVGG9QulE9uG8RwZ53ekV1KWn27tAygs5Pcvr1oDwBX97fd1ONSslm6K8VVd2WcXa7IGMOOQ1lk5eQ3SPxKKXU2mjdvHjfeeCN+fn7MmjWLbt26ER0dzT333ENBQQGPPPJIY4foUptkdy8wH/ACAko9qmOkMaavMWaA8/wRYL4xppNzzeJ3aBzQyXlMAt6uRbxKKaXOQVucJSLaNvcvU/7q9X1d+6H+Xtw3ulODxqXq3uhuLVz71/SPomdkEACrnbV3x3S36yfvSs5yLWMEsMXp1jxtwwEufXUJY19d2lAhK6XUWWXlypVceeWVAPzwww8MGDDAdWzy5MkEBQXx/fffs2zZssYKsYwaT1BljKnLDtkTgYuc/U+BRcDDTvlnTlfplSISLCKtjDEHK7yKUkopVcq6hKMAXNm3dbljHu5ubHtqLEt3pTAgJhRvD/eGDk/VMS8PN3Y9Ow5jwNPdDU/3sn/TjwnzJ8jXk4TU48zfloy3hxvRoX4cTD8BwKzNhwBISj+BMQYRncRKKaWKbdq0ifHjx5Obm8vUqVMZOXJkmeOhoaE8/PDDTJkyhQcffJCVK1c2UqQlTrtlV0SeqGUdA8wRkbUiMskpa1GcwDrb4n5IkUDpgTT7nTKllFLqlD5fmQDArcPaVXjc18udMT1aVji7rzo7ebq74eVR8vWma0vb6ax4PG9Mcz8OZOTwS8JRQvy8GNEpjOy8QnILClkbf9R1XmKaTYA3J2XwyfK9FBbpFCVKqXNbr169SEtLIz8/n4kTJ1ZYZ/LkyRhjzohEF2rWsvs7Ecms4rgANwBPVHJ8mDHmgIhEAHNFZPsprnWycv/bOEnzJIA2bdpUcTmllFJNTWGR4emftrIpKYMvfjcYH8+SFtppG5IA6Bsd3FjhqUb24a0D+Xp1AtcNiAage+sgNuzPwBjoHxNCQPGkZUkZJGfl4uku5Bcaft5zhDbN23DZG7YrnrenOzcO0u8YSil1NqnJmN33KTtG9+RHM6dOhYwxB5xtMvA9MAg4LCKtAJxt8WJ4+4HoUqdHAQcquOZ7xpgBxpgB4eHhNbglpZRSZ6O07Dw6TJnBJz/vY238Ubr+dRZHjtllZI7lFpBfaGjb3K+Ro1SNKTLYlz+P6UJ0qP05GNwu1HVsRMcwzmtj/xDyxSrbC+B2pxfAocwc9pVarmiNM+5XKaXU2eO0W3ZrM1ZXRPwBN2NMlrM/BngKmAb8Fnje2f7gnDINuEdEvgYGAxk6XlcppRRAfmER/Z6eW658wDPz2Pf8BP69Ih6A286PaeDI1JlsfK9W3P/NesBOYpXsrLH73S+2F8DlfVrz7pI4fklIJ8Q3veC2AAAgAElEQVSvpHv7ZmcSq6Iiw1M/baV7q0CuGxiNUkqpM1eNJ6iqoRbA986EDx7Al8aYWSKyBviPiNwBJADXOvVnAOOB3cBx4LYGjlcppdQZ6vefxbr2dz87DjcR2k+ZAcA1b//sSmKuH6hdT1UJLw83dj87DhHB3U1oHexb5nj3VoGIQMbxPJbvPgJARIC3ay3mL1bF88nP+wA02VVKqTNcgya7xpg4oE8F5anA6ArKDXB3A4SmlFLqLLJiTyqLdqQQHuDNqsmjcXOzUzysnjKaQX+fT6wz0dCE3q3w9dJZllVZHifN0hwV4sv+o3ZCKjc3oV+bENbGH8XP6xie7sLg9s35ccMBiooMc7clu847ciyXsGbeDRq7Ukqp6qvNOrtKKaVUo7j5w1UA/PfOoa5EFyAi0IePbx2Ir6c7kcG+/P1XvRorRHUWecVZc/nj2wYCEOhj2wKO5xXSKSKAts5437gjx/il1IzNC7bbxPebNQmMf20pKU5vAqWUUmeGGrXsiogPcBkwAmgNnAA2A9ONMVvqLjyllFKqrPjUbAqKDGHNvIgJ8y93fGTXCLY9PbYRIlNnq4Exoex7foLr+ehuLVi4IwWA4Z3C6NYqEIBfEtI5lltA76ggNu7PcE2G9vC3mwD4aPleHh7btYGjV0opVZmarrO7HBgKrALeBf4DFADPi8hcEeldl0EqpZRSxV6euxOAF67W/2pU/bi0R0vX/u+Gt6NrK7tW71sLdwNwZd9IAFbGpZGcleOqO2fLoQaMUiml1KnUpGV3jTHmiUqOveysn6uzgSillKoX0zfaSflHd2vRyJGopio8wJuvJw0hOSuXiEAfwooMAPtSjwNwQWe7zOHR7DwWOS3AAIlpdtzvibxCfvvRarq1CuDJiT0bOHqllFLFTrtl1xgzHUBErj35mIhca4xJNsbElj9TKaWUqp1DGTkUFBn6Rgc3diiqiRvSvjlX9GkN2EmrSk9E1SHcnz5RQWxKynDN2NwrMoi8wiIAPl8Zz+p9aXy6Ih4716ZSSqnGUJsJqiZXs0wppZSqE8VLvtygS76oBnZ1P9t1eVBMKCJCcyf5/WH9AQCGdmgOQEpWrmviKihp7VVKKdXwTrsbs4iMw659Gykir5c6FIgdt6uUUkrVijGG/EKDl0fZv8nO3WrHRP7KSTyUaiiTx3fjT5d0xsfTLmXVOyrIldQ29/eiY3gzAHYcymLzgQzXeYt3JnPz0Biem7mNr1cn8sPdwyqcWE0ppVTdq0nL7gEgFsgB1pZ6TAMurbvQlFJKnYuem7mNdpNn0PmxmcQ8Mt1VnltQyJ6UbFoF+eDtoWvnqoZXnOgCjOvZyrV/Qedwure2MzavjEslK6eA852W3tTsPIwxvLs4jowT+Xy1OqFhg1ZKqXPYabfsGmM2iMhmYIwx5tN6iEkppdQ56o5P1jC/VBdQgJhHprPv+QnM2XIYwDWOUqnG1KVlgGv/oUu7EOrvBcC/nBmbR3WN4Oc9qazem8bmpExX3dX70lz7uQWF+ocbpZSqRzUas2uMKQSai4hXHcejlFLqHDVtwwFXojuoXSgz7hvhOvbczG1898t+AO4Y3q5R4lPqZHufG8/OZ8bROti3TKsvwIWdw/HycCMtO69MgrvlgE18Nydl0OWxWYx/bWmDxqyUUueSmiw9VCweWC4i04Ds4kJjzMu1jkoppdQ5JSsnn/u+WgfAx7cOZGTXCADev2UAv/8slncXxwFwVb9IIgJ9Gi1OpUoTEbw8xPW8X5tgfklIB6BTiwB6RQaxNv4oK+NSAejROtCV7BZ3Z956MJOiIoObm6CUUqpu1WY25gPAT841Ako9lFJKqdMy5fvNALxx43muRBfgku4tuHFQyczLD13apcFjU6q6/u/izgAM7xgGQFgz2wFu7lbbBX+087OdcTyfnYezXOftS7VtBoVFhhN5hQ0Wr1JKNXU1btk1xjxZl4EopZQ6N2Ucz+fHDQdoH+7P5RWMx33uqt48d1XvRohMqdNzYedw9j0/wfW8X5sQZjtjzSODfQn2s8nvlgMZrNl31FVv28Es2oc3Y+Kby9iclMm8By6kY0Szhg1eKaWaoBq37IpIuIi8KCIzRGRB8aMug1NKKdX0/WvhLgCu7a9r56qmZXyvkhmbR3YNp090MADzttmx6aOclt7NBzLILSh0TWT18fK9DRypUkpV37Rp0xARhgwZUmmdnTt34uPjQ+vWrcnMzKy0Xn2rTTfmL4DtQDvgSWAfsKYOYlJKKXUOeX+p/WI/6YL2jRyJUnUrOtSPri0DCPDx4PHLe7hmcP7ISWav7hcFwN6UbDbuL1mbd+8R11QoxKdmk5OvXZuVUmeOESNGICKsW7eOnJycCuvcdddd5Obm8sorrxAYGNjAEZaoTbLb3BjzIZBvjFlsjLkdqDy9V0oppU6ScTwfsF083XWCHtUEzbr/AjY9cSme7m408y47euyCzmG4CexJOcY8Z1yvCGxItJNcbUhM58IXF/Hr91c2eNxKKVWZkJAQevToQV5eHrGxseWOf/bZZyxcuJAxY8Zw/fXXN0KEJWozG3O+sz0oIhOwE1ZF1T4kpZRS54qv19gZaf84skMjR6JUw+gdFeRqxQ3w8aR9eDN2JR+jyBgAzou2MzobY1wzNv+SkE5hkdE/CCl1Jpj5CBza1NhRnJ6WvWDc83V6yREjRrB582ZWrFjB8OHDXeVpaWk8+OCD+Pj48NZbb9Xpa9ZEbVp2nxGRIODPwIPAB8D9dRKVUkqpc8KPGw8AMLFvZCNHolTDeGxCdwBev/E8ALq1st379qRk4+4m9GsTAkBmTkGZ7sy7k48BkJNfSELq8YYMWSmlyrngggsA+Pnnn8uU/+UvfyElJYUpU6bQoUPj/yG7Ni27R40xGUAGMBJARIbVSVRKKaWalON5BRgD/qW6cRYVGTYnZdLc36tc906lmqpB7ULLzNjcMbxk1uUO4f5EhfgCsDkpg1V70/B0F/ILDdsOZtKlZQDXvrOCTUkZTLtnGL2jghs8fqXOeXXcQnq2GjFiBAArVqxwlS1btoyPPvqILl268PDDDzdWaGXUpmX3jWqWlSMi7iKyTkR+cp63E5FVIrJLRL4RES+n3Nt5vts5HlOLeJVSSjWw/MIiYh6ZTve/zabH47OJeWS669iC7XZG2tLr6ip1rrm6f0mvht+PaE9vZ8bm/8QmAnBetG3pXZ+YTn5hEZuSbBfob9YkNnCkSilVIjIyknbt2nH48GHi4uLIz8/nrrvuwhjDW2+9hZeXV2OHCNQg2RWRoSLyZyBcRB4o9XgCcK/mZf4P2Fbq+QvAK8aYTsBR4A6n/A5sC3JH4BWnnlJKqbNATn4hnR6dWa68OOF9b0kcAHfqLMzqHBYV4sd9ozpyYedwrh0QTa/IIAB+WG+7+N88tC0AcUey2ZxUMmPz9kNZrv1N+zM4llvQgFErpVTZrsz//Oc/2bJlCzfddBOjRo1q5MhK1KRl1wtohu0CHVDqkQlcc6qTRSQKmIAd44uICDAKmOpU+RS40tmf6DzHOT7aqa+UUuoMN/bVJa79T24byNs39XM9f3PhblbvS8PdTejUIqAxwlPqjPHAmC58evsgADzdy341G9U1gvAAb+JSjrFwR4qrfOdhm+z+vPsIl/9rGde8XXbcnFJK1bfirsxffvklTz/9NMHBwbz00kuNHFVZpz1IyhizGFgsIp8YY+IBRMQNaGaMqc6Kwa8Cf8EmyADNgXRjTPGfJPcDxX16IoFE53ULRCTDqX/kdONWSinVcL5clcA+ZxKdHc+MxdvDdvyZPK4rz83czouzdwDwxOXdGy1Gpc5UF3QOZ8lOm9j6e3vQ3N+L7Yey+GJlPACD24Wyam8aRUWGL1bZGZu3H8qiqMjgpjM2K6UaSHGyO3Om7cX18ssv06JFi8YMqZzajNl9TkQCRcQf2ArsEJGHqjpBRC4Dko0xa0sXV1DVVONY6etOEpFYEYlNSUmp4BSllFINJb+wiCnf22UZNj4xxpXoAtx5YQcmj+sKwJD2ofxmSNtGiVGpM9mL1/RmcLtQvp40BIAerW3X5tTsPKBkBueDmTnExqe5ztvlzNgcn5rN8t3aLqCUql+dO3d2JbeDBw9m0qRJjRxRebWZ/rK7MSZTRG4CZgAPA2uBF6s4ZxhwhYiMB3yAQGxLb7CIeDitu1HYNXvBtvJGA/tFxAMIAtJOvqgx5j3gPYABAwaUS4aVUko1nPeX2rG4t54fQ6CPZ7njd17YgTsvbPzlCJQ6U7UI9OGbO4e6nndrVdLVP8Dbg77OJFY7D2dxODPXdax4xuYLX1wEwA93D6NPtM7YrJSqH8eO2T+wubu788477+DmVpt21PpRm4g8RcQTO772B2NMPhW0upZmjJlsjIkyxsQANwALjDE3AQspGe/7W+AHZ3+a8xzn+AJjjCazSil1Bntl7k4AHnFacJVStXNt/2jX/nNX96JdmD8AMzYeBGy3Z4CtBzNJTCtZg7d4kiullKoPTz/9NIcPH+a+++6jb9++jR1OhWqT7L4L7AP8gSUi0hY7SVVNPAw8ICK7sWNyP3TKPwSaO+UPAI/UIl6llFL1LDMnn/xCQ1gzb3w8qztBv1KqKkF+njx0aReGdwxjQq9W9HRmbP7v2v0A3DjQJsPbDmayeGfJcK5Ve1MBOHIsl7u//IX52w43cORKqaZqwYIFvPzyy7Rv356nn366scOpVI27MRtjXgdeL34uIgnAyNM4fxGwyNmPAwZVUCcHuLamMSqllGpY/3HW/vy/0R0bORKlmpa7R3bk7pH235X7STOajOgcToC3B/Gpx9l+qKTd4UR+IQAP/XcDC3eksGRnCpueuLTBYlZKNS1btmzhlVde4dChQ8yePRtPT0+++eYb/P39Gzu0StVZx2pj6SJvSil1Dvt+XRIAv+oX1ciRKNW0jegU5tpv5u1BdKgfCWnH+XlPKt4eblzSvQVxKdkAbD5gE+CsnAKKR4OtjEslIfV4+QsrpVQlZs+ezYcffsiSJUsYMWIEc+fOZcCAAY0dVpXOvFHESimlzkrGGLYcyCTYz5Nm3rWZ/1ApdSqv3XAevaOCXDM2d3UmsYpLyaZlkA9BvnZyuIzj+aRklUxideRYHpk5+dzw3koueHEhuQWFDR+8Uuqs9MADD2CMITMzkwULFjBs2LDGDumUNNlVSil1WpKzcnhuxjY2J2WUKV++244PHNUlojHCUuqcEurvxbR7hjOkfXOgZHkigO6tAl2zME/bYHtbeDp9nzckpvPjhpKJq1bG2UUu8gqK2O0sXaSUUk1FjZNdEfEUkftEZKrzuNeZnVkppVQTVFRk6PPkHAY9O593l8Rx2RvLiHlkuuv4u0v2AHD78HaNFaJS56xr+pcMHfjdiHa0a27H0P1j9g4AHhzTBbBr8S7dWbIG77aDtovz5W8s4+KXFzN7y6GGClkppepdbVp23wb6A285j35OmVJKqSbGGEOHR2eQcSK/3LG7v/wFYwzbDmYBuGaKVUo1nCBfT76ZNIS/Xdad/m1DGRATAthxugBXO8nwzsNZ7ErOcp23cX86xhh2HLZlszaXJLt5BUUUFBY11C0opVSdq82gqoHGmD6lni8QkQ21DUgppdSZ5/2lcRSvcj7vgQvpGNGM/8Ym8tDUjUzfeJClO1PIzCng7pEdGjdQpc5hg9s3Z7DTrdnH0x0RXP9uw5p5A7An5RiZOQV0imjGruRj7EnOZufhku7LG/enA7D9UCZjX11Kx4hmzHvgwoa9EaWUqiO1adktFBHXtxoRaQ/oLAdKKdXEpGXn8fcZ24kO9SXu7+PpGNEMgGsHRHNVv0gAMp3Wo3tHdWq0OJVSZY3v1Qqw43sBIoN92bg/g5SsXMb0aEFMcz9SjuUyr9T6u3ucGZzfWmiHJexOPkZegbbuKqXOTrVJdh8CForIIhFZDCwAHqybsJRSSp0pnvlpKwCPX9YDN7eyC3y+fF1fXr2+L8F+nsx74AJ8PN0bI0SlVAXe/HU/pt83nDWPXgxAl5YBrmMtg3zp2jKQtOw8th+yXZhHd7WTyxUVGTY4LbwAa/bZSazSsvMY++oSHv9hc0PdglKNoniJLtVw6us9r02yuwzoBNznPLoAy+siKKWUUmeO75y1c0d3q3iW5SvPi2T938bQMSKgwuNKqcbTo3UQ7s4fqUr/G76iT2siQ3wBWLYrxdZ1xtvvOJxFfKk1eNfGHwXgncV72H4oi09XxHM4M6dB4leqobm7u1NYqJ1VG1pRURFubnW/UFBtrrjCGJNrjNlojNlgjMkFVtRVYEopdU4wBtL2Ql529eofjYfDW0oG4tWz7YfsTK3924YgIqeorZQ6k/16UBv6tQnm5iFtCfL1ZKAzidXR4/kMjAmhe6tAAJbtsrM1/3pwG4AKJ686edbmxTtTiE+t5u8xpc5gfn5+HDumy3A1tOPHj+Pr61vn1z3tCapEpCUQCfiKyHlA8befQMCvDmNTSqmmLTcLPrsSkmLBJwiu+Rg6jq68/s//gjmP2v3IAXDbDPDwrtcQ/70iHoB7RnWs19dRStU/EeG7Pw5zPR/aIcy13y7Mnx6tbbL775X23/150cF8uSqBnYeyyC8s4kD6CXpFBrEpKYPpGw9yy9AYAF6Zu5PX5u8CYOcz4/DyqPvWGaUaSmBgIEeOHCEgIAB3dx2a0xCMMaSnp+Pv71/n167Jb6NLgX8CUcBLpR5/AqbUXWhKKdXEvTPCJrr9boGiQvj8KpsAV2TT1JJEt/M4e94X11Z9/XWfw1vnw5c3QHpijUIsbr25oFN4jc5XSp25gnw9aRdmv1z+4aKORAbbVpWENNuF+eJuLejcohm7U46xPjGdgiLD9QOjAUhMK+nm/PHyva79L1bFN1T4StWLgIAA/P39iY+PJz09nYKCAh3DW0+MMeTm5nLw4EEKCgoICQmp89c47ZZdY8ynwKcicrUx5ts6j0gppc4FB9bD0b22RfeKN8AnGH5+Hb67E278smxdY+DbO+z+H1dBeBd4Mhj2Lob8HPD0KX/9n/4EsR9BsxaQvAWO7ID71pWrlpNfyFerEygsMtwyNKZMi0xOfiFHjuXRo3Wga8yfUqppWfjgReQXFuHpbv/tRwR4k5yVC0CIvxdtm/uz8/Axvl27H4DurQMZ0DaE2PijGGM4nJlLZk6Bq8V34Y4UbhvWznX9R77dyKIdKXw1aYgrsVbqTCYiREREkJWVRWZmJsnJyTqGtx55eHgQFBREREREvYzZrfE6u5roKqVUKb/8G6bdU/I8MApu+R+EVbIUz/d32u0NX9ntJU/ZZHfHdCgqgtK/8Fe+bbd9b4KIrnb//Hvh5zdg6T9h1GNlrx2/wia6CPzfRvjqeohbBPE/Q9vzXdWW7Ezhlo9WO88Mi2b+h39O7EDL/hPBw4tv1tjW4Iu7tTjtt0MpdfYoTnTBjtN9dd4ubhxkW3B7RQYxd+thvnZ+H5wXHUzPyCBi449yKDOHOVvsskW3D49hynebWbIzxXWtlXGprvMe+u8Gpv6h5PePUmcyESEwMJDAwMDGDkXVkg6qUEqp2sjPgQ8uKZvoegdB5n741wBY/X7F56Rst/sxzvg5Eeh5jd3/5ZOy9WdPttsJL5WUjXQS3FXvlb/+x2Pt9jdTbavvJU/Z53P/5qqSeiyXP3y+1nlmeMnzHT73eo6WM39H4b9/BcCXqxIAuGVo20puXinV1Nx/cWe2PTWW567qDcCE3q1cx6JDfRERejqzNs/cdIj/xNpkdlzPVnRtZWdkz8m3rWAfLivp3hzrzOhcbPuhTJ6buY3dyToRkFKq/miyq5RSNZW4Bp5tAfud1tHfL4AnMmByAtztlM14EBJWlT1v8fN2O+LPZcvHv2i3c/5aUrZjlt226AWepWYp9PSB0PaQm1F2nO/26XbrHwEd7dqatOpjt/vXuGZxvv3TWLLzCvn8jsHs6/4+V7sv5cfCISwp7IV7/DLy139DQtpxmnl70LxZ/U6CpZQ6s/h6lUzK0yG8mWv/zgs6AHBJd9vbY9nuI2w5kEmAtwc+nu6M7dESKFmXd8nOFERglLN+74H0E65rjX11Ke8ujmP8a0vr92aUUue0Gie7IuInIn8Vkfed551E5LK6C00ppc5gS16ED51kss+v4fF0iOxfcjy8C9zkjPb4aIztmlxs2St2e+HDZa/pFwq+IZB3DDLs2rZ893u7vfbj8jH0v82J5Z8lZcWtt7fPKlu359V2m7iawiLDhsR0AIb7xtsuzsC4x6fz5/w/AHDwu0c5kV/I81f3quwdgKzDNZ74Sil19lg9ZTT/u3sYvxlie3kE+XoCsGB7MlDS+jukfXMAFu1IISUrl9yCIkZ2iWBi39aAXZ4IYNfhkj/Q5RUWkVQqCQbYeTiLzUkZ9XhHSqlzRW1adj8GcoGhzvP9wDO1jkgppc5kBXnw3khY4Py6u/Fr+NXbthvyyTpdXJKQTv+T3e6PtduwLhUvG1TcVfmb39gW4dxM8A6seOzv4LvsNtZJhJPWQupuiB4MzTucVNcmsaz5gC9X2+7Jky5oDx+MsuW3zcTDw4OP772MdONPG7cUvMhnQq9WlGMMfP8HeKkzvNoTVrxZvo5SqsmICPShb3RwmbIRnUqWLfrDRfb3TXH35vnbDvPZin0AXN6nFcM72rpr9toW3y+cIRI3DrLr+BbPDwCwcHsyY15ZwmVvLGPrgcy6vxml1DmlNsluB2PMP4B8AGPMCUrW3FVKqabn4EZ4JhwO/GJnT35wF3QZV/U5l70Cnn6w9hM4tAkWPG3Lr3yr4vrFLbAHfrEtwgA3fFFxXQ8vmzTnZkBaHHw8wZZf8Ub5utED7XbHTNesqvfHJJQcdyau6hkZhN/Q2wHYdrOdpKOcD8fAhi9tEu4XBrOnQNre8vWUUk3W6zecx3UDonjmyp60bW5nWXZ3E4L9PNmXepypzu+ZCb1au4ZC/JJgx+0u3GFbhB+/vDsAa+PTXNd9fNqWktdw1u4tZozhUEZOPd2RUqopqk2ymycivoABEJEO2JbeSomIj4isFpENIrJFRJ50ytuJyCoR2SUi34iIl1Pu7Tzf7RyPqUW8SilVc0tfgndH2P3e18PD+6BZxKnPE4Hf/mj33xluuwx3vQyiBlR+zt1rILgNuHvB8D9Buwsqr3vxE3b7+nlQcMIm1uFdKq7buh/kZZGYGI+vpzt+051Jte5aVqaa1+DfAeC+uoLJr2I/Khmj/PA++PV/7P6cx8rXVUo1WSH+Xvzjmj6urs3FLu1ux+0ezMghprmfazmz9uH+7Es9Tn5hEfGpx+naMgAfT3d8Pd3ZtN92WS4sMiSkHcffGTM8y1nnu9iolxYz5Ln5/GXqhvq+PaVUE1GbZPcJYBYQLSJfAPOBv5zinFxglDGmD9AXGCsiQ4AXgFeMMZ2Ao4CzoCR3AEeNMR2BV5x6SinVcI6nwRsDYL4zo/G1n8JV71XcbbkyUQPs5FUdL4YBt8PVH1ZdP7wz3LcBHj1cksxWput46OK06DZrwfxRP9D5sZnEPDLdNZuyS7+bAbjNYxa/jj4C2ckQ3BZanjQuNyTGbhNXly3PP2HX7wW4cwm4uUOUM055+09VxwlQkAuZB05dTyl11npgTGfX/uOX93DtF3eD/mT5PgAuc8b5ntcmmMycAgqLDHOc5PbGQW3o3sou+VJYZCfV25NyjL1HsgH4T+x+ipzyYsaUfa6UUlCLZNcYMwe4CrgV+AoYYIxZdIpzjDGmeI55T+dhgFHAVKf8U+BKZ3+i8xzn+GipsE+dUkrVMWPsWNh/tIPUXdC8EzwUBz2uPPW5FYnsD7/51unW7HPq+m5uZdfarcr1n8N969hy3XLu+CGFvAI7GdaU7zcx7PkFJfXOs8nule7LmZL8kC278euKr9lhNBTl22S/2MdOl+0255fM8AzQ1lk+6cD6ymPcNBX+0R5e7gbznqzefSmlzjotAn1Y/ehoVk0ZzciuJb1fxve0ye2zM7YBcN0Au45vcVIbuy+N6ZsOAnDrsBgu62Prr3WWLHpzwW4AokLsrPRLdpWs57s7+Rjd/zaboc/N50ReYb3dm1Lq7FOb2ZinAWOARcaYn4wxR6p5nruIrAeSgbnAHiDdGFPgVNkPRDr7kUAigHM8A2he05iVUqpads2Ffw2En+4HD1+45mO4Nxb8z9BfP25uENqeX70bi5e7Gwv+fCGdIuxyIUnpJ3htnjPuzd2TfxTdRJQcwb3guO1O3aJ7xdfseZXdLn/VbtMT4cA6u3/7zLJ1Rz5qt1u+q/ha0x+Eb++ws0z7hsKylyFucQ1vVil1posI8KFFYNk/6pVOfFsH+RDhHB/nTII3f3sy6xLS8XJ3IyrEj0ExoUDJjM9Ld9uvmR/81g4BWeiUA/z+s1hO5BdyMCOHf87ZUU93pZQ6G9WmG/NLwAhgq4j8V0SuEZFTNlcYYwqNMX2BKGAQ0K2ias62olbccv1URGSSiMSKSGxKSkoFpyil1CkYA9tnwDsj4Itr7KzGw+6HyYklid8ZbGVcKnmFRQxuH0r78GbMfeBC7h5pZ0h9Zd5O0rLzSD2Wy1t543mj2X1w3Wdw3b8rv2Cva+12/Vd2+6EzWdZlr5Sv22aI3a77vPyxA+tgzft2/95f4I8r7f4Pd5/mHSqlzmbubsJ/7xrKb4e25bM7BrvKi7s3v7ckjqT0E64W3eLytfFp5BYUkpKVy6CYULq0CABgnbN8Wn5hkat7M8CiHSVJMNhEuOtfZ/Lekj31d3NKqTNWbboxLzbG/BFoD7wHXIdtra3u+enAImAIECwiHs6hKKB4UNd+IBrAOR4EpHESY8x7xpgBxpgB4eHhNbshpdS5a+dseGsofH0jHNllk9xH4uGSJ8Hds7Gjq5bi1ttHJ5T8/fChS7sS4mfj7/f0XN5bEgcIkaPuhO4Tq+4m7eENYZ3tuCVcIPwAACAASURBVN7YjyDL+bU84Pbydd3c7YRax1P/n72zDo/i6v74Z+JECEQIARKCa3CH4lqhtMhbp+7tr/5W31J3d3elpbTFiru7OwECJCEQD9Gd3x9n7t5dEpyi9/M8+9zZO3dmZ7Ozm3vuOed7wHVQCOGn3aVte7OUQwqLgaBwyNoJRXllTmUwGM5d2iZE8MylTanrRJ6AGMHVwrWvZGhrCW/28/WhUrA/i5IyGLtSwpuHtK6BZVlUCQtkpSNqNWWdTD1v7VqbauFBbNmrf1dSswuYtDaVgmIXL45bX26er8n1NRjObU7Es4ujxjwYuB1oi86vPdT4aMuyKnkc2xtYB0wDhjjDhgN/Ott/Oc9x9k+1za+SwWA4WaStk3I9Pw6DrGTo+SQ8skWM3KDw0311bnILS8guKD7smHlb9wHQsGpFr/5l/+vr3v5k5lbqVgnl8lY1ju6Fh3wprRKlumXqocc2GijtBo8Q58/76G1VPxigw53Srj5E2LMifRNsmgwHMo/ueg0Gw1nJc4Oa4udj0aZmZTrW0ekiyov72SwpbdancQyg6/nmFpYwYbUYwtd2qEn72nLszv35AM4Cn9YTXJik/SUlpS5qPTaOBk9NcJ/Dk/15RW5xLIPBcPZyIjm7vyCGak/gA6Tu7j1HOCwWmGZZ1kpgETDJtu0xwH+BByzL2ozk5Cqp0i+ASKf/AeDR471eg8FgcJObJmG0H3aA7XOg073w4Hro+jAEhJzuq/Nixsa9tH5uEs2fmUjtx8aSU47Ru3qXeDh6Nyq/FNLaZ/tRJSyQkABfPry61dG/eNVE6OGUFLp+rIhsHQrl8V3wsbQHMnSJosd2HTTWEdxf+u2hz/dhJ3i/DfwwGD7qDMWmtqbBcK7Sq1EMG54fwG93dPLqH9RSJFzW7cmmeqUKVA4JAKBL3SgAZm7cy4yNewny9yEuIph+TcQYVqHME9eKuvMfd4qI3qS1qe5zfzhdwpqLSlw8NHKl1+v+syaFVs7vbnk+loJiI4JlMJwtnIhn9yvEwL3dtu2ptm27jnSAbdsrbdtuadt2M9u2m9q2/azTv9W27Xa2bde1bXuobduFTn+B87yus3/rCVyvwWA43yk+IPVgX68n+aX1+sK9S6HvcxAYeuTjTzFZ+cUM/3IhAb4+RIYE4rIhcUTZydeXc8TrcUPnWuWeJzjAj4VP9GbNs/2p73hKjppuD8OILEjocvhxkZIfTNIsad9wwqm7PVr2bxvqpJskH1TaSPFRZ0hbI9uJwyA7GSaPOLbrNhgMZxW+PmVlWoa21lEoN3ROcG93ayC/IT8t3EFGfjE9HfGrDo5nd0VyFgXFpezcf4DWNSu783//8ajbO85Rfo4MCSC3sITcwhL3vif+WA2I5/j7g0q4PTRyBQ2fmsDHM0wOsMFwNnDMxq5lWT2dzWDgUsuyLvd8nNzLMxgMhpOAbcOCT+CVWjD3PYhpCjeMh6tHQkTt0311h+Th31YAEuK36Ile7v4bvl7kNU55KzrWPs1q0e3vkPaZCCg5INs9Hit/bJPLpM1J8e5f9j2kykSTW2dITePAcFjwkXyOBoPhvMHP14cVT/dl3mM9ufkC/VtdO0oicGZtEoXmq9vXBKBSsHh+J69LZYyT56vq+YYF+rErU36XCopLWZ+SQ+e6ke7zznbOlZVfTHpuIbFOHvGfy3Rkyo59+fy2JBmAl8ev97rWA0WlXPvFAvq+NcN4fg2GM4jj8ex2c9pLynlcfJKuy2AwGE4Ou5fBO81h/CPgFwCDv4A75kDNTkc+9l8iu6CYXxfvdNePPBQTHSN2UMvqWJbFkid7AzB9w17SsiWsd19uITkFJSRWD8enHM/IKaXPM2D5gO1M9O46hOcWoK6Tzzv/I91XWqxVmrs/DtVaSLJdA6e+7zZTrshgON8Ir+BPbHgFrz7LsuhcN9LZ9l7oq1G5Apn5xXw/fzsAFzmljXo0rIJtizH713IR3OvfpKp7/98rpe+XxeLJ/d/FjakY5Mdij9/pt6dsdF8TwIaUHPe+VyasZ9amdDam5vLKBG0Iu1w21325kDu+X2LEsAyG08AxG7u2bT/tbD5r2/YNng/guZN7eQbDeUZxAawfC0lzjs2LVVpy5DHnI7PeFDXgzO2Sj/tIEiQOOdJR/youl03LZyfxyG8rGfzRXJ4cvarccct2yATrgnpR7r7I0EAuc3LY2r04BYBv58mE7poO8f/mZR8dfoFSXqjVdXDfKohucOixjS+Vdu1o3fecfq90/6/e7uTIQSw5rAaifA+MwrPBcF7w5fVteWZgEybd39Vroa93I8nbXe6UJlL1fFvXrAyI1/eHBfK7eVmrGsRHBgOw1DFq/1kji4zdG1ShRbwck5lfBMCE1RKJ8vYVLQAYtTTZ/brTPEoeTVyjc4PHrtrDzI17Gb86xV0z2OWyefyPVbwwdu0J/hUMBsOROJGc3d/L6fvtBM5nMJxbFObCtJfg896w8LMjj9+/FT5oBz9fBV9feHR1SPdvk/M/Fwnvt4U9K498jCIrGTZPhr0bj/6YswWXC0ZeD1OegfB4McB6Pnn4UjuniMf/WEWpy6ZP4xgqBfvz/fwdzNmcXmbc204poUf6NfTqf+s/LdzbD49cwddzkwAY1ibu37voYyGiFgx8T0oRHY7AUAirBhlJ8nltn6f3/e+gCnNVm0q7dfqhzzf3PfkevFgNfiunPJLBYDinCPTzZXinBOpW8dYhGN4pwb19ZTv9uzigaVUAJqxJYYVTtig0UKpeNogJY09WAbZts2R7BpYFFQJ83erP41enUFzqIr+olPoxoXStJznDKvomLbuA7fvyuaJtHLWiQtiVecDtxf3dwyCesXEvAL8tSebHBTv4bNY2sg6I6GB6biGfz9rKgSITAm0wnEz8jjzEG8uyGgJNgPCDcnQrAkHlH2UwnGcUZMOX/bXITvIi8A2A1sPLH5+/XzyQJYVwybtihC7/AUJjoPfT5R+TvkmOcZVIDdMVP8MnF8B/k6BC5cNf36w3YMpzgOM9bnszXPi6rs9wOHYvk2MzkuQ9+frLI3uPePJ8/cHHH3z9nDZAwod9A8E/CALCIHsXxLWH+v0gOOLIr3kslBTCt5fCjnkiQHXFT3ItZwg/L9oJwCfXtGZFciaXfTiXqz9fQNLLF3mNU5OixBplSyBNfbAbPd+YwUgnd+y5S5tgHc1nd6bR5kaY9rzc63/dLX03TJC6vQcT1wF2zpdFpIMFr2a9KQsbAIEVYfXvUKvbob9vBoPhnKVWVAjPDGxCSnYBD/ap7+5XHl6lcXBJ82rufZ3rRrEhNcf9+6yM2YsSY3lq9GrmbE6nSlggAP2bxuLrYxEW6EfSPokk+XGhhD63qxWBv68P29LzWLojg9Y1I9xq+aBzjMd7lDr6Z3UKw9rGccf3S1iUlMG0DWn8cHMHALfBfFb+vhsMZwjHMwNsgOTmVkLydBU5wC0n46IMhrOe7y8XQ3fge9DgQnitDvx9LyQOhYDgsuN/vwkKsmDIV9D0chm37i+Y/Sb0+l9ZI9S2pSwLwPXjIKEzVGsFf94JP10FN44v+xqK324UY6BmZ+h8Hyz9BhZ9LoZ1t0cO/76S5sB3l0FpobwvHz/Js9y/BSrXhKJcee4qcdpiKCmS8SVFUJwvfQBLvtLnrdtbDOOYxpKrebzG6YFM8XTv2wSthpPZ+3VS9ubjcoG/r4Wfrw9+Phb+vj6EV/CnQkA5RtW/yDQnhK1TnUh8fCxaxlemdlQIW9Pz+HP5Li5tISHKKvyuW/3ocs9TOzqUmQ/34Nt5SRSWuLimQ81Tcv0nnU73wLz3tKEb1wFqdix/bJ2eYuyuHQ0tr9H9mTu0oTvgVSlr9EJV+b4ZY9dgOC/x9O560r1BNNM3yELiYwN01MzQNjX4cs42HhslaSX/aSse4Qin1NGUdWluj+ugFmIkd29Yhb9X7CYjr4ixjhjWRc1iSYgK4bv52xm9bDct4yqTnltEm5qV2bI3l23pYhzP3KSjeVYkZzKsbRyLkiSMetE2nSNc67FxAGx6YQD+vqc/MslgOBs55hmlbdt/An9altXRtu15RzzAYDhX2LEAvuyrnz+0WZdQ8WTFL+LJrd1dchcBuv0XZrwixujQr73H714GW6ZCdEMxdEEM4trdJWxzxc/Q4krvY+a8LW1cezF0AVpeLeffMVcMS7+Aste28DMxdEOi4bq/xKis2xs+6gTTXhCF3Kh65b///P0SXg1wx1yIaVL+uMNh22IQZ+2CfZth91LI3g07F0gY98bx4nUG6HI/dH9M8kCPhvRN8EUfOJDBrGo38eSGS9g+d9JhD4mpGEiLuErEVAyiT+MYLqhXvnF5snh/2mYAnh/U1N33zY3tuODVafzfz8vdxu5bkyS0/O6edQ95rvjIYJ68uPG/eLWnAP8guPxzqc0b2wx6HSKKAaDdLTD9RVhzkLH7dqK0sc2h/W2yHddO6ienbzr0/WwwGM47Pr6mNV/M3kbtqBCqVdKiVw2reodC92tS1b3dOLYia/dkM8VZrKwdLZElHWpH8PeK3YxcspNNabmEBfoR6OdLixpS5mhlciarHK9uYo1wKgUHMHldKiWlLkpdNg2rhrE+JYelOzK96qcXlbooKXWx3kP86q/luxncugYul82ynZnUjwklLMj/JP91DIZzkxOJ7VtmWdZdSEizO3zZtm2TLGU499g+D77q7933el2pP3owf9wq7SXv6r7uj4mxu+YP8d56emq/GSjt4M+9zzPoY3izIUx6ytvYLS3WNUeHj/E+psOdMP9DMQp6j/DeV1wA4x6S7Zsmae+pjw90fxRGDpdreXBdOX8AJAcWoOdTx2fogrzvwDCo0lAejTwE3HNSYO2fsGkSbJ4Es9+SR0Rt8XRf8FD5BrzLBXPfcf9NHi25jZ+3dqNdQhDD2sRRMzIYPx+L4lKbEpeL4lKb4lIXmfnFbEzNYdr6NLILStxCT4or28Vzdft44iODCQ3wcwug2LZ93CFlSn1ZTZYA4iKCqR0dwta9ebw1aSP39KzrDmFum3CSQ7zPROr3lceRCI6QUPjNk2TRxLLgh2F6/20z9XbXh+G7OVJu6qLXyz9fUZ4stBhj2GA4bwjy9+WuHmUXES3L4qXLE3ln8iau75zgVfP3uo41edTx+HoKBl7aojpP/LGaF8eJ8nJvJ7/Xx8eiSlggK5Kz3L/lfRrHsHR7BpPXpTJqqZQy6tWoCnuyCkjPLXQrPocF+pFTWML6lBwmr9MiV4u372dw6xp8NGMLr/2zgeqVKjDn0Z4YDIYjcyLG7nfAeqAf8CxwNXCIWbLBcBaTlawN3eFjoNYFMMLJo1z2vbeXacd8aSvXkrBehWVJ2O+GcXJMq2ulP20dFGaDfzBUTfR+3Yqxkn+Yt1cm5gFSV5C/75O2491ljb/ez4ixO/utssbuqJul7fuCiAh50mQQjARydotgVXR97/27l0nZFx9/6PrQIf5QJ0hYVfHMtb9N3u/aP0WZev0YWSiY8YoeW6sr1O4h3rvNkwEoxYfLC0dQXLUVfw1OpJmzun4kbNtm3Z4c/liWzIyNe9mYmgvATwt38JOTh3Uk2iZU5qXLm1G3Sughx/y9QspaKDVlT365tSNtX5jMO1M2MW6VhMM9OqBhmXHnPfEd5D7cs0K+X5v+kf4HDxJZq91d2lUjyzd2Z70BU56V7Xa3wYWv/ltXbDAYzhKubBfPle3KCuv9p20cvyzeSUmpzSuDm7n7QwP9CA30I7dQqiF4GtFtEiozblUKbzpROu0SIih1Sf7txzO2OGMiWLM7m+kb9jJvyz4A7uxRl1cmrGdFciYLt2mhvsVOiLP6P6LqBQPs3J/PpLWpXN8p4fSXnzMYzkBOJAGgrm3bTwF5tm1/A1wEJB7hGIPh7OPbQdIO/UYMXYBHtkl7sGLyX/dKO+TLsue5+C1pJ3uEav7iGMrDviv/tVUO7XinDIurFJZ/L9t9ni073i8AIh1PVeoa3Z+2Htb9LeHLne4u/7XUNfxyddl9n3aX9sZ/yj/2ZBMQAi2ugit+gMeS4ZJ3oOlgvX/bTMnTdAzdMb69aFzwJY3b9mTsvV2O2tAFWdFvXK0iT1zUmIn3d2PbSxcy5p4uPDOwySFzZg9mUVIGvd+cwcvj1+NylV8y6nmnxMRD/cqW44kOC2TEJRKSvCktl3pVQrn1gtpH/R7OG3o8Lu3iL+F15+/Y6V4Ii/EeZ1kQHgcFmWVLeKWt04YuwMJPvL8rBoPB4IFlWfxxZ2f+vqeLV+gzwIuXJxIVGkCfxjFei51DW2sV6LAgP/x8fWgeJ/+Xtjp5ux1qRZIQKYvYqqTRkNY1ANiUmsu8rfsIC/KjUrA/OQViUHuGNm93xLEueHUaz45Zyy+Ld57U920wnCuciGdXJRhkWpbVFEgBEk74igyGM4nx/xWxo8aDxPupCI4QQajdS2H1KMm1LcqH9A2yv3qrsucKqwrBUZCfDvu2iErxPsnhpF7v8l+/w50w8UlY9h1c+j786RiqicPKV6wFuOgN+HaghB3fvUj6Pmwv7VW/Hvq9NnbCqdM3itJy5QR5rmqbBkdCjdaHPv7fIjAMWl8vj8FfiJJz5k4IjiTXrxJ9P17F7qwC7utdj/t61z/S2Y6IZVk0rR5O0+rhhxQ5OZip61N5bNQqPp6xhY9nbGHmwz3ctRtBajSmZhfi72tR/aDJkuL6zrXoUi+KZTsyubhZNbNCXx7xHcRru9Sj3m7fQ5R3b3SJRDlsnuL9/fpQVE7pcBfU6wPfDYKpL8CVP/5bV20wGM5RBjavxkAPVWdF9wbRVAkLJC2nkEf6S5ROxYNybCsE+NK+VgRfz01ix/58wgL9iAqVaK11e7KxbYiPCKZG5Qr8syaVtJwCr+MXbNvvFW49aW2q2zP9/fztNKsRfkwLvwbDucqJeHY/tSyrMvAU8BewFjCxYIZzh3kfiHBOeHzZfFqAoY6a8CgnR1d5ebseRtF40If6mL8dL/ClHxx6vI+vlFAByUld8aP3ecqjtjM+faMo1a74WZ5H1S/fCPdk8BfSjnbeS0mRvs5bph7+2FOBZUF4DajZkfzw2gz8ci27swq4t2fdk2LoHi89G8aw4PHe/F8v8ap3fW0a41fp0hJP/yWewzeGtSj3eEXdKmEMbRN3ylWizyoGvg8BoaIE/ljyoce1c76XKhICYMZrerv/izrcecPYw79mSZHkyhsMBsNRYFkWCx7vxaoRfbnWQy1fbQ/vKG3rBF0msH7VMCzLIio0kAVOCHPPhlWIdkoe/b1C/qc81Ff+121Jy3Ur/AMs3SGhztM2pPHk6NUMfH/Ov/X2DIaziuP27Nq2rWb/MwATb2c4t9gyDf55XCbVd86V2rEHUzkBwmIhZ4+E1a4ZJf3dHzv0eev3gzq9YMsUeV6zs3fOb3lc9Qu811rycAH6v1L+9Xhy5c/w0xVaqTa6Edw67fDHACQOgbnvwvbZMPkZ2LNc+i94UHt6zwAKikvp9/ZMdu4/wN096vJA37KhwaeD+/vUJy4imId/W8EdPyz12ndRs9hyPQCGY6RSHDy+68jjVF76mj9EAd22paYvwL3OfW1ZEN9JFMxz0yC0StnzTH5GSoABXPYpNP/PCb8Fg8Fw7mNZVhnF5GcvbcL1nROoHSXhy1XC3PquXOV4ZeMiKpCeWwhA65qVyS4o4fv5O/houuT6DkiM5fWJG1m7J5vt+/Kd8wSyL68IgG/nJrnPuWVvLnWiQ0nNLmDC6hSu6VDTyxtsMJwPHLOxa1nWA4fbb9v2m8d/OQbDGcCBDAltBPFmBoYdeuzVI+HjLvCNU3K6+2Oibnw4rvpV6tr6BUDLa498Pf4V4KaJsPEfKTVUtemRj2kwQNSgl3wlRmr/l+U8R8MN4yVPWU3w298htX7PEPbnFXHpB7PZuf8Ad/WoU24O7OlkSOsa9GsSw3tTN/PpzK0A1IwM5s1hzU/zlZ2HRNaTNIT923RJq5hEb4G2VteJsbtqJHQ8KAd/7nv6ewCitF6nR/lGscFgMBwBy7KoE+0tZLjg8V6s3ZNNjwbyu9KzQRWW7ZBa613qRrE7U8KXlQFcJzqU4ABfdu7PRykSDGhalW/mbSevsMQtsggwZ3M6daJDue27JSzfmUlyRj5PXHSWl6wzGI6R4/HsHmbmbzCc5dg2fOGUQRn0MUQfwZCqmgg3TYaFn0qIcIc7jvwavn7Q4fZju67wGtD2pmM7pvVweRwrASFi8KasgAoRFFaM541x65i7JZ2akSHc16se9WJOz8/AyuRMrv9qEfvzinikfwPu7H7oOrSnk7Agfx6/sBGPX9jodF/K+U2fZ+Dnq0TJe8VP0nfjBO8xjQfC6Nth1W/exm5hruTLA9y1EDK2w49DJV3h6pGn5voNBsM5T0zFIGIqag/vjV1qsWRHBt3rR+Pn60NchF6oViHNtaJCWLM7G4A60SHEOloQG1Nz2JV5gNpRIWxNz3MLWi3fKcbzn8t3u43d/XlF+PlaZXKJDYZzjWM2dm3bfubfuBCD4Yxg2ouS69roEu/atocjrq08ziV8/aC6iFENeW82q3Zl0axGOGNX7mHsyj28d2VLLjmFIbnFpS5e+2cDn87cip+PxcfXtKJ/09hT9vqGs5T6AyTnXhm6Xe6HwIPKQ6mSXru9w875rIe0TS6XRS+18LVpotR2PlQER94+WPYtxHcUQS2DwWA4BkIC/fj6hnbu55ZlUa9KKJvScrmzex0AEquHu43ddrUiaeEoPf/llCZqHleJrel5bErNYZuj/gyQliPe4VKXTavnJgGw7tn+RifCcE5z3Dm7lmXVBz4CYmzbbmpZVjNgoG3bz5+0qzMYTiXbZsLMVyGiNgwup3TQecg7kzexalcWfRrH8Nl1bVixM5NBH87hnp+WEejnQ4v4SqzcmcX6lGz25xWzO/MAGflFRIUGsjenkIVJIrLRv0lVdmbkM7xTAv2bVj3qleS0nAK+mZvE57O2UVjionFsRT66phU1nXINBsNh8fGBq38VobnaPbwV1T1pcrnk3Ksa01m7ZNELtBAdiCr72tFS67dOj7LnWfod/OVR2mvIV6LUbjAYDCfA6Ls6sz4lm9Y1IwCp0fvzIik11KthFRrFVgTgqzlJAFyYGMvMjXvZvi+fSWulrFF4BX+yDhSTW1jirusLMGldqtGTMJzTWPbBNQiP9kDLmgE8DHxi23ZLp2+1bdtHkVD479GmTRt78eLFp/MSDGcj2XvgzYaAJSGL0adP2fdMweWyqf34OADWPNOPkEBZG5uyLpWbvjl537Gr28cTGx6Ey5bV5j1ZB9iQksOmtFzyi0oBqF6pAg/0qc9gpwahwXBS2Todvr0U6vWVEOVXa0P+Puj3EnS8U49LXQsfdZSyYwcLvuXvh1edXOCuj8DM1wAb/rf/0GXCDAaD4TgoKnGROOIfqleqwJQHu2FZFgmPalX5Nc/049ovFrB0Ryb9m1RlwpoUHupbn9cnbuSHm9vzzdwkJq5NBeDKdvG8dHkiSel5DP9qId3rR/PMpad1Km8wHBWWZS2xbbvNkcadSJ3dYNu2F1qWl6pbyQmcz2A4PeTtE5EpgCFfnrOGbmFJKdvS84gIDqCKR37QofhwutQAvqhZrNvQBejVKIb3rmzJmJW7iQgJIDIkkO4NoqlXJYzwYG+PrW3bFJW6SM0qZP62fSxJymBFciYbU3NwOetsPyzYcchr6NWwCjdfUJuOdSKP4x0bDEdJ7e7SbpoImyeLoQvehi5AjCPscnDIM8DXF0vb7lbo+YScY/EXMONV6HEIhfaSQokoiWsHQeEn+i4MBsN5QoCfDxueH+DV1yi2Iuv2ZBMVGkhIoB9NqoWzdEcma/dIuHP72vJ/9ODQ5mVOyaL3p21m+758vpm33Ri7hnOKEzF20y3LqgMiBmdZ1hBgz+EPMRj+ZWwbCrJk27+CPPc/jGG3Yz582U+2ez51zoYcTt+Qxr0/LSO7QK9Hjbv3AhpXq3jIY16fKGGcrwxuVmbfJc2rHVXOrmVZBPr5Eh8ZTHxkMMPaxJUZU1TiAsDXx8LHkmMMhlNO/1dgwn/h+8Hy/KbJ5Y9reDGsHwO7l0M1p25y8mJIk1rKDHDKzfd9Tozdee+Xb+yWFsPzHqrOj+6EoEN/Hw2GM4odC2RxqOtD3kr/c96B+R9Lfnz7W3W/bcPKX6BayyMLPxqOi0cHNOTJ0au4rauT11tDFtB27M9nQNOq1HeEJVfvzibzQDH1qkhJorwimRdMXpfqPldWfjHhwf5MWJ3Cn8t38drQ5oQGnojJYDCcPk7kzr0L+BRoaFnWLmAbcPXhDrAsKw74FqgKuIBPbdt+x7KsCOAXIAFIAobZtp1hyaz3HeBCIB+43rbtcpbUDec1K36GP247urGR9URB2fKB1b/p/o53yz/tc5C07AJu/W4JRSUudxgTwIXvzuKz69rQp3FMmWPmbxXPVuPYiv/6P7gAvyOUajIYTgUdboe0tVJv94IHDi061/k+MXanPg/XOL8hn/eS9urfpHYviPBV5QTISJIFuIM9t0r1XTHmfhjyxcl6NwbDyWPhZ+Aq1VUEXKXwpXP/ZiXD5Z/IdmkJTHLK1I1/2NvYnfo8zHpdtp9I8TaQdy2VSIh6ff7d93GO061+NLMe6en1XFEvJozwChJ5NW/LPvbmFHJl2zg2puYyYU0KxaUuMvOL3eMXJu2nT+MYbv9+CSCK0SMGNjlF78RgOLkc1yzTsiwfoI1t272BaKChbdtdbNvefoRDS4AHbdtuBHQA7rIsqzHwKDDFtu16wBTnOcAAoJ7zuBURxDIYoPgAvN8ORoSXNXQj6kC/F6U+rHXQLb5vkwjReBq6V/wE/V7496/5NHHzt4spKnHx3U3tuLtnPZJevoim1cWDdMu3i9mUmlPmmCs+nQ/Aa0PLenUNhnOWge/CVT9LWPGhZAaBDQAAIABJREFUUEbw5knirVrl/JZYvmUn653ukXbOu979uWk6FPppKQni9ZsEcu4fr4DX6sHGicf+XgyG42H/Vpj9lpTeAti1BMY9JFEPmU7KyZapevyaP/T2+jHe50rfpLcXfKK3l3ytt7N2ifL5D0Ng6bcn5S0YhJiKQbSrJYJWwzvWBET/YlfmAQDiI0PcBvBkJ3/3+k4JAKzfk01aToH7XIscsUmAdXuyWZmc+a9fv8FwsjguY9e2bRdwt7OdZ9t22dly+cftUZ5Z55h1QHXgUuAbZ9g3gJLMvBT41hbmA5UsyzL1Rs5nSkvgw07wQlVI36D7//MDjMiSx71LpV7mgJfh6Qzd//BWMYKbXwWXvAv3r5X+hheevvfzL7NmdxYrk7NoEVeJC+rpVd4x91xAMyfEqc9bM92hxABrnXIGAX4+NKlm8ggNhjI0uUzaqc/D707967sWlh3X+gZpl37j3f/jf6Tt/Yx4gmt2luc5KXrM6t9h43jISxND42BcrrJ9BsOxUnxAxNUUX10Ek0fApKfk+WYPw3bZD9JuccTZ/IKgtFDOARLWDCLsBrDyV2lLiqAoBypWl+cbxulzznlbb89+y/va1o+FT7rBil+O660Z4NfbOpL08kVEhkp93noxuvRa3yYxtE6oLOMWi7JzXyfSa31qDjM3prvHbnDq9WYXFDPgnVkMfH8OGXlFp+Q9GAwnyonED06yLOshy7LiLMuKUI+jPdiyrASgJbAAKV+0B8QgBlQiU3Vgp8dhyU7fwee61bKsxZZlLd67d+/xvRvDmc+sN+G5SJ0bFxYLj+8Rg7XRxUc+PiRSjODLPoLWwyG8zK10zvHQyJUAPNi3rOjWX3d3ca/q1n9yPCCCUg+NXAHApPu7nqKrNBjOMgY5QUYqLLPtLRBVt+w4H1+Z4Oft1VoCRfnaq9vlPmmbOzW9l/+oj/X0mO3fKqGjiu+HwAsx2vgwGI4H25aF41draYM1R+q0smGCtLuW6PFqe/scabs+LO1GZ6wyYtvd6t2vPMEtrhIDedtMfU41JjhS7nPPCiG/Xgd7lsMft3r3G46bB/tIvnR8RDAVg/xJrC4L2tM2yNy5gyNitXVvHjv35wNS07fEZVNYUsrvS5Ld55rkkeO7fGcmBcUev1EGwxnEiRi7NyJ5uzOBJc7jqOqRWJYVCvwO3GfbdvbhhpbTV+YXz7btT23bbmPbdpvo6OhyDjGc1eSlS7jylGfkuW+A5Pw8uB4Cgk/vtZ3BlLps1jkqjJ5eXU+W/0+HXfZ4fToXvTubtXuyeaR/A1PL1mA4FP4V4N7l0PNJuHY0XPT6oce2d9Ispr8s7eg7pL3gQT1G1f/d7CGKtX4MxDQVPQGA3cukPZAhIdSlRfDnQWrRxQVyjhLjcTGUQ/5+EWBb+5c83+sRHbV6FBR4TMeU0Zu6RgzUgFDYuUD60jdKDnriUHm+ZrQYowcyIDwefP0gsCKkyGIrG5ySOInDINYRdSvKl2Myd0ClmiL8Bjr0OTcNXB4FPnYeFDkx5TmY+JQxgo+RxBrhbHx+ADMe7g5AA0e0CqBSsD8+PhbxEcFsTsth/Oo9RIcF0qOBzB82peby94rd7vELt0lEwPhVexj0wRyu+mz+qXsjBsMxcNzGrm3btcp51D7ScZZl+SOG7g+2bY9yulNVeLLTpjn9yYCnfGsNYDeG84cVP8NrdfTz22fDU3u9xS3OMyas3kPCo2NJeHQsPV+ffsjV1E9mbgHgxs61Dnkuy7JY/1x/YsOD2Jaex9o92bRLiOCObnUOeYzBYAAiaolnq06Pw4/r4Bik8z8U7+7a0fK8x5N6TGAYVKisPWbb50lbr4/OA1aho4sOErHK0p4WvugtxszPVx77+zGce5QWi0CaYsHHshjy67XyfKeHcZK8CFJXex+fkwpZO0TUsUojKMyWCIPifKjeGipLHii7l2rDVkVZxXeQNnu39uRG14fGl8r25kl6AadOT6lxDdowXvadtG1vlnblz/q61vwhURVz34XFXx7Tn8QgKUqq6oGPj0WUE+Lcr3FVABpWDaO41GZjai6VKvjTJkGCNjek5LDD8fYCbr2P96dJmcKlOzKxncUH27aNp9dwxnDMxq5lWV2OsL+iZVnlFuhy1JW/ANbZtv2mx66/gOHO9nDgT4/+6yyhA5Clwp0N5zguF3w7SItPtbhawpWrJp7e6zrNrEzO5PbvtSD51vQ8Gj41wR1u5MknM7YC8FC/w9cNDvL3Ze6jPfnr7s78cHN7frmtgyn/YzCcLHz9tdfq5XhpB74HPgf9+63eRtqcVClXBJIbHOcYDcoQXuMYy5d9Kq0ygg9kQMoq2d482Tun17ZhzwqT53u+8c0l8E5zWPe3PN8+V+87kAGpa/Xz3UshbZ1st3SM4aRZ0laqCTUcYbZFn0tbrZW0sc3FO7v4K3muFmeaOXnpS74Wg7uSYxg36C/tmtGw3AnDbzZMH7fOEblS3ud+L0q7w8Mwn+UxffQUvgK5x/dv8w77NxyW6Q935+NrWvHS5TK/ah5Xyb2vY51IGlQV7++qXVnszyuiQ+0I6kSHsClNRMzWp2jZHlW/956fltHwqQmMX2Wm7IbTz/F4dgdbljXXsqz/WZZ1kWVZ7SzL6mpZ1o2WZX0HjAEO5XbrDFwL9LQsa7nzuBB4GehjWdYmoI/zHGAcsBXYDHwG3FnOOQ3nGlm74NnKsNWZxN06HQZ9eDqv6Ixh4Psy4f377i6sf66/u/+CV6eRW6hDvtKyC8g6UEzVikEEBxy5dJBlWTSrUYnOdaOMoWswnGyGfgN1ekGVxmLotrqu7JjEIdLOe1+r2sY2lzrhfkFSngUgdZWElCrjQOU8eircAuzyyCr6/Wb4pKvkZhrOTWxbPucl3+jnO5wIgYXOwsielXp80hwJUQYpkbVnhfbsKmNXCUbFNocGA2R7/CPS1u0tba1u0i5xjN1a3aVtdIm0M16RVnluI5wAwDWjYPlPsh3fEfzEu+jOC05dDf7B0l+xupQFU6SsBB8/eXgKVQJ8NwjebQGjzXTxaAkN9KN/01h8fOR//yXNqrn3XdqiGjEVgwD4em4SLhv6NK5K1fAg8otKyS4optRl4+ccO3/rfmzbZsxKMXJ/Waxld1wu20sM02A4VRyzsWvb9v3ARcAeYCjwHPAAUh7oE9u2u9q2vegQx862bduybbuZbdstnMc427b32bbdy7btek673xlv27Z9l23bdWzbTrRt+6hygg1nMZsmw1uNZTsmEZ5KlyL0BlbvEoGbhlXDSKwRTpC/L0kvX+Te3/Tpf9zbb02WWrrlCVMZDIZTjK8fXDsK7pxXvqELOv9xrlOmqHprva96ayjM0iGptbpCsKMHqQwY5fG9zvGIbZ4irW2LsjNAQaYWyjKc/RzwKP+yeQqsGgl/3yvhy54Kyyo3tzAL/B0thuRFsG+zhM+HO9lie0Sc0F16Sxm/VZtqxXCFGtPlft1XpbGOWPAL1N5c8M5Rj20ubXEeVK6la1PX7Q3YkDRb8nWbDZN+NQfISRGvLYgxrSIm8qQuPCVFsG2GbK/8Wao3GI6Z+MhgXhmcyKtDmtG6pvzOhAT4uvc3ig2jqVOpQQlW3dA5ARDv78bUXPfYWZtE0bnUZVP78XHUf3L86TV4S4th3xbvvsVfll0snPgkvFjdWygQYP04eDYSfrqq7LkLcyR6wtx3ZxzHW3oow7btz2zbvt627X62bQ+ybfsx27Znn+wLNJxHzH4bfhgs2z2ehDtmSwigAYCn/pSJxwuXeYdyb3tJl05KeHQsOQXF/L1CVlWHtK5x6i7QYDAcPz6+3gbF5Z/p7epOyOjP10ir1JujG0pOJYhqrX+wGMKgcx9T1wC2CPuB9qaBGBUjwmHUQbXKDWc+f98Hr9TUYcXKyAMRktqt013ITRVBKIC2N0qbsgpyUyC6EdTtJX27logX1bKgosf/jppd5P5U3tkqTeQ5yKLLlT+LkNrwv72v8dbp0n/VSKjoUTWyz3N627PGvcrn/dpZxFWh0A2d52v/1LV4290mub6gSx55ljQ6+G8CEv489QUjanUU/KdtPMPaaMmcbg20yGWHWpHUiZYSRt/P3w7AFe0kRWNX5gFmb9Yli0pdNrZte/VNWqtVnNOyCyh1naTPI2+ftyGbtw++6AvzPCIDv7kE3mulw+5z98KY++Hv/4Otzv1SWgJz34OiXPjr/7xfY/QdshCzYax3WgDA6/XhqwEw9n4MZxYnosZsMJw8/rgDJj8t29eMgm4Pn97rOcOwbZtlO2QVv3XNyl77LMti1Yi+7ueJIyaSW1jC59e1MSHJBsPZxDWjxAN29W8Q6SES19QJcU51cnLV5F95f5Od0M9aXbWXLNMJH1zo5DTeIOXFvErJTHlW2pU/eyvxGs48xtzvLU6mwoZXOMJNKt8WpD7t7uWyrRZQNjmRP8GREBgOWxzPf6U470WWik4IqzKAffzAz1kouewTGPq1GLGeNBggRmtIlHd/cIT01+/r3V+7G9w2E26YoO9lgOYHectqdpK2vpOys/xHXZ4rrr2HeJvzXpT427VOq+r8gqhNT3kGZr6qawgbjpoH+tSneqUK3N2jLj4+Fl3ri/G7ZW8eoYF+JESGEB0WyKrkTMauFB3Z6zslAJCccYBp69Pc55rilCxasj2Ddi9O4d6flpV9wYMjUCY+BZP+p59v/AdeSYA57+q+12qLIau0C+a9Jws//zymx6jQfvVdWven3qfEzjwXSQqzoKRQtosLJDpGMfd9vZ22XoTbQBZkPBdUbBtmvQEzXyv7Pg2nBGPsGk4vrlL4oh+scP6B/d9K/U/W4GZRUgYAFzeLLXd/WJA/i57oTX2nYHzH2pH0dorDGwyGswT/IOj1Pz2JV1RroberNNZetYQLpP3c8XApj2/t7jIpKy3R9VJrOAJYSgG3tESXkgFY+Yv3a+5epkNGDaeWkiJdggdkMWPxlzD2AZl4q8k36NDj9A3g6+S97l0PGc5np8Lm574nbWxz7xrzVRO1+BRow7f/S9DjCbhlqt4XHCGiacr4PRFim0PNjt59vn5w40RodgVc6xE+GhwBPv4SvZCbAmGxEi6tDPNNk6TdPFVKHimF9LQ1+hyzPUStVv3m/bp56fD1xVLX13h9y6VulTDmPNqTh/pJnd6q4UHufdUqBeHrY1EtPIiM/GJ2ZkjN5pbxInS1IjmT8au1UJVSdH5/qtzjpWv+xHYU6A8UlZI36l4R81NRC5k7JL1jzjuQ4oTWT31eRNbUwoWn2Jpa5NjqYbSmrpE6zu7njkG80yPrUuWFKx2ERgOlVSXhVLRMT0dJ39MoVtca6sy7Dv5tnfKsXPMmj/JyhlOGMXYNp4+SIlmF2zkfKkTA47t1KYPzhC17c2n7wmR3KaHU7IJyx308Q0Jzbut66JJA0WGBTLy/G0kvX8RPt3b4V67XYDCcJu5eAr1HwA0eoZqNB3qPUaJAStl52wzIS4PIuvI8JhH2OUaUCvns8oC0W6fr86wbA592F6GfQq20ajhFfNYT3m8DCxxhqbUeht/W6d6LEKVF4nHK3CHlgQLDJY9beXrreIQog4S+q3BhgBrtJBqg26MSpqxycANCoNsjOr/2VBHfHi7/RIcoKzp7hJP2f1lvV06QhZ19W8QLpxaKIupoDx/ItuUrAlk5e7yN2t9uFOXptX/CCo8wf4CVI+HLAbLP4MVNXWrh72txg1PesGW8RJ3tzSmkfa0ImlWvSEtrE5tSckjNLqRikB//DRnDLSnPwIFMpm3YS1trPR8HvI31VX9wuWj0vwn4rXBUuhc6BqTSHAD9+aR4iK0VF2i1etCLH55jts/1NmxBFo3UYlGVJlI/GrT3t/cIadc7Ru4q5zqaXyX1ootyJQcYpJQWSOQDeIfUT/SIJDClsk4LJ2TsWpbVybKsqyzLuk49TtaFGc5xig/A24kiuBKTCA9vln+u5xEFxaUM/3Ihe3P0Kn37F6ewfGdmmbFTnRCgxBrhp+z6DAbDGURUXTFEKnikMQSESD4liIKu8vgqo3fULdI2cPL6Ix0l3KxkPaFrd4sYAZ6TxfkeOW4H1/U1nFxKS8S4/fs+3ae8TsrbnrVL79s6XefjVm0mraqXG90QYptBfrp8xmGxEKpzLQHp8xRJU0JTPR6Dq0dChUqckfR6CoZ8JeH4TQbp/iaXS/uek9fe2NkX6/xtctO00dv6ev1d2OWR0+zpoVv6nd52lcKom2HHXPH6emLbMO5hGPfI+VnmKCeFp1oXs+mFC7myXTyUFnNb6gju9pWFmW4Noqk55zH+CHya1kskLa1PwyjuKP2RftZ8XE748TDf6e5TFm+fjx8lBFqOwNNeWbDZt9Gj7NSeFVCU530te5bLQ7Fvs5SgcpXocpVJs7Xx287RKEheLK8RWQ+qNJS+vHTYt1UiBFQqifptVN7a8OpanXzzZLkXVHmteCdaQZXOKi2RBccwJwpBlfNSrP5ddBPUeMO/wnEbu06ZodeBLkBb59HmJF2X4VymtBg+6izhSPX6wu2z9CTtPOLxP1aRnHGAVwc381JVHvTBHJIzdN1cpcLcrX50mXMYDIbznGtHwc1T4ZJ3dJ/yxuU7KrWd7pVWheVtmSaTrIrVJRQ0so6EBIJMErfPEY8ZeBvBIKrPY+43NXuPl8ydElKsPOYbxorXdclXUJgrC8EKVT5q91IIrSrbqashb69sK2+nCqGMqK0n954e/WhnIu8Wn6oG962CR7bpHO+zgaaX6zxeRce7vJ+rhR7l0V71m/amNb9CG7sqV1MpVatSSjs8RIdUOKvCM1R2yVdS0mnhJ6Lc68m+LTDyBl0G6mwjda3+PQDYMB4+6KBTIgDeaCDlzJKc34eVvxK7ayIP+Y+kAgUMaxOHz0bxbnYtlMWEQdV1Hm7uKimv1q6ifp2UFZOoa+32upQFa7eQkiRGrx0QCns3sG21GJ35dZx507ZZkjMLEhHgKhaDF7SoWsoqrS6uIhvUZx1ZR5fQ2jBeVMJVOH9EHTFkS0tkEUkpgze5TNpVv2kjuG4v+T4FhOk0AlVGrtkwyTMvzPZWa/7NEYz79VoM/x4n4tltA3S2bftO27bvcR73nqwLM5yjuFzwaQ/YvwXa3CgryWfTP9uTyKilslo/tI2oXnoavF1emeZWKPxunqgd3tur3im+QoPBcMbjFwg1WnsvGFqWntA1vFh799SEbsHHotAb6+QCKwXnzJ06P63hxVLfVyndAuSkwsjhYjws+Pjfe0/nEjkpYsQqvuwvxpESB9vm4enZ9I93ri5IiGZGEgRVFK/+7uXawFDCTescFeTY5pKvrVAq3pd9AvGd4EIPgZxK8bp81dlMSBT0e0m2h36t5xPq/l/+gzZ2q7cRgwO0kq7K72x3G8Q56T/q81KKvQMdIaJl3+vXneOxuOQZZgtiBK4ZJWWgPBeFZr8FPwzzLgsF/16e8MHnLSmCeR+I118x6jZ4r7W+puQl8FFHEX9Sx095TjygKlIkV4tNuYXSNo53dy0cXERUSIBebAMCKaJFkRaiCsmS+7x6SRJ7bYlYK9q+kGY+krI1uVSMyrVzx9LA2sk2Vwz5EY0hL40/xooX9MNM+bzsnfMp2r2aDN8o7VlV0SnVWonHdf8WCe0PCtf6BdOc+6ZGW73YoRYuVGRAghM587ezsKQWUaIld5k1o2D+R7KtBNYaON/L7N3aEG5zo762ZCecOvmgaqqeERyGk8qJGLurgaon60IM5wG2DV/1lxCt1jfAxW+d7is6bSzcJv9YLqgX5aWY7FlGqM7j40jOyOeXxTtpWr1iGRVmg8FgOCRDvxHP3X88JujK6FUejqZOCGgNJ5R14wStaNv6eohpIttq0jvrdX2uuR4qqAaNp4FRmCsesJeq6/5sx9BQdZFVziBIaK0KUVZGmfI+VW0GMU3FM5TqCC8FhuqSUiDGbl0PcTNVuqdaC7hxvLfy8blExzthRJb2toEsDvgF6Xu9coKIWvn6iSK1Mjj2engEVXj0tpnSqjzMFld796uw1QoR4k3PTdWfb94+yeVUrPMIZ508QhY0Zryiz/NKLXg+RhvYuWkSOaFeC2RBZPZb+jVsW4zWnQv1mA0TYOT1kL5Zv94LsfBqbW1w/3kX/PO4lN8BMX5X/ixe0DlvS5+nUJ3K+1ZCX4XZ8tpbPETL1N/Ro+RPWNoSyNqJJ72CtxKWKedb4qqHLy58KcWvKIeNVgIl+FI5cw1NrCQAviiVuVCFvSvxs1xssmuQ5Ce5wW2KxEj8brcIdhbvWUuAXcDqoqrsCWsqL+gY4Xb1Vtobm5sqpbb8HCE32wk/r91di7YptWUVBdPOMfCXO7+jbW/WbyqmKdgu/ZsZ5wi9KcN56bdiCIdWFT0aFXWglNGVkdzhTmkPLp1lOGmciLEbBay1LOsfy7L+Uo+TdWGGc5CfrpBVrsRhcMnbp/tqTiufOIJTT13c2Kvfsiy2vKgN3i6vTAPgmYFNT93FGQyGsx/LEs/dwZEzbW/R202duub1+0m7eYp4wkBCYJXnUBlgapIbFC4iP57sXCReq9Q1nLfMeQeeqaQN2VUj9b5dS71zDfMc71hGkuTRghgvSm1WCUUpz2OlOB2enp0seYagFyoAwqqKQXfPUrhlml6sOF/p/YzeHvSR3lYCbvu2SJhpXAcxgpWRsna0Ln1TvbX8TStU9lDwdbx1jS+F+k7upsoZXebUAVbCb+oe8BSAU9783UvhwH4oLYQ1jgjZ5BHiif7WIy/5m4ulX5VdWjNKjNYv+mgD+Lcb5BxKnXjjBCg5IN5V9b1V3mylSuy50KLKl3mKem2dBgcO0hDJ3qXLWlm+cv+CGMbqnkxdrb2WTn7s211tt0E81fHaXhogr5kaVIf1rjgi7Awa+shi0AJXIwD6F4gBuM6uyYqSBAC6+so1ZtnB2NVaEZAnoc9r7Fr8naFrAwNc8WOSd7SD8tiqxSQQ7y/oHHjQ352qiaIEDpJ361kvuqdH+Lqn6Jvanv4SYGsvsDK6Nzhe8NWOKnj3R6Xd4ZGbbNvw63B4p7ksoBhOiBMxdkcAg4AXgTc8HgZDWcY8ID++dXrB4M9O99X8a6zelcX7UzexdEfGYcdNcQSn6seEldnn62Ox/rn+NK8RTlRoIA/2qW+8ugaD4eRw4WtSJ9UzX1OJEqlQxBptZZ8qSbNlqoTT7tssuWyq7q+HN4cveovHYuT1p+BNnAHYtmhPfD9E9812FnGnOEaWpzGRNNO7xjHIJDYvDcJriCBOyioxJkCHSyr12djm3p7ZeGey3ucZ8WB2+6/+PCPr6BDm85kOt0vN3Vumeef6KoEuJWqlSntFOcbaujF6kUF5x1UOZ/5+Xdu41XV6oUgJDG10vHZdH5JWGYaeue/KU+qp8Ky8yJudmsF2qagFZ3p4SJVxtHqU7kvfKPeiqvGq0hCSPby+W2XR3B1VAJKS4Hk/qpJkaWshIFT3qTHqt2DnQl2iR/1tCrIAG2Iaixczda2+99vcAEBA8nxZCAuPZ39lMSrvD5G/VUmVJqy34wFo77OO4sDKuPChwDeUSpYsEC1z1eGfgkbuy93jI4Gl2ZUauvvmuxqxIqUY/CoAkGJXZkFSho5gAa5f0ZB9uYXQ9RHpaHChLGaA3CvNroCrfvVeJPzvNhFGu/egesANBsDgL6Dv83C5x7zWvwKEexjdvZz6wD6+8j3fux5ynbz7mERZPATvlJHVv8uiS0YSjPEQrzMcF8dt7Nq2PQNYD4Q5j3VOn8HgzZx3YfEX8qW++rcjjz9LScsu4OL3ZvP6xI1c/uFcEh4dW+64zWkSrnRBvahDnivI35c/7+7C4id7c4/J1TUYDCcLyxIPw8H5mmoyD1JfFbT3Y8s0CXcE8ZLUcur7KmVRz9wzVb5DsfwnbQSezcx9H95vqz1gG8aLB2vzJJmQ2rZ46UB7zlQYKIgRoxYHEodJu226tFEN5DMpOSBew6Bwp5atx4Q7vpPO+QOo5xhZNdrAk6nQ4/GT+GbPIer0KGv4HxzOrQwfgKj6IlA0z8nTVQad8vqu+k3f79VaaiNaGZk75sniQ0CI5MRnJ0sYsTJkVDRFUb724oOUiwIR7vTsS5qtn6v7SY0F+W4qkS2QUlS27T0mZaX20KpazEmztCJ79dbynktLJIy3emvp3z5PG7ZK5G7bDPmOB4XrRQKVE12lsRi8hVla+TjK8WruXCAe7JgmPHn7cADiDsj7qVyvE1NLdS1xnxiJeFtWrEtR7qzYmkX7K7if59eQ36DFlS92982hOZvScmDQB2TaITxbLKJPma4KFA75gVuL7md6VlU+nL4F6vWm+KkMbi56gJfGO3/XkEgpe6UWMBSBYfI5+wdRhsQh0OkeHRqtuPEfSdfr9bTOEQZ9H73uiMe1v1XaSjUlTFyFnM/0SBlRCyiG4+ZE1JiHAQuBocAwYIFlWUMOf5ThvGPd3xJWExoDN0/WK2jnIP/5VEJQXrws0d1XnsH7+j/yj2l4x4RTcl0Gg8FwRK76VUSLYpqKgQAQECxeku1z9OSrzY1a6EpNwpT3UQn8qMl30hwYfTtMftq7NuyZiGeubUEWvFzT22s77QWZ5Ksw0V0eBv7aP73EeAAxLrJ3QbCzqLl/m/Z2tb5e2llvShtVVxuy+fu0ErYSDgMIi5GFihsnwrDvytZYNhw9liX3e61u8rcMidT7WokhJqJg4TrqQRmpM1+TcGYV/eDrL7WNU1aKtxS0qJHyhiYvknvHL0iLwu1ZIYslgeHyvcvYpkWiVDhw0kwdHl2xuq4PnLUDQpz8+x3zPGooO57PvRscMaZKklecskqfR+Wgbp+rv6fKYN/uGNbRDaByLTHUlWFf14k02LlQriOqgb5npzt1j6u1EoMXJBokIFTmfFUTxZADiG1OSGhFr4+ja4d2THdpY9e3ttz375ZIKHdRdFPiYiLJLypV4DILAAAgAElEQVTlfxUe58eSHoRc/AIAsw4kcEPRw9xX5UviIkLZsjeP1PiLaFH4GeNc8nu0elc2k12tmeiSz2PdHrmWb+dtZ/K6ND6ZsZW8Qq2Q/NeK3czelM4JEV5d0vUueMC737NeNEjJONDf573r5DN2yi4R01QWCYz6/QlxIpbHE0Bb27aH27Z9HdAOeOoIxxjOJ1LXwi/XiIDGrdPLXxU7R8gpKGZbeh4Bfj5c1T6eVSP6uvfd/8tyr7ET1sjKbc+GVU7pNRoMBsMhCQyVcjR3HFRqSIXLKsGZkCjtFVbeI+W1UhM5lSOo8n8BlnqUYXm9Przb8vTVJ927wTsXcfSdkmurRIHW/ikers2TpESQq1SHiSrBo+0eJWp2LdUGR4yjr7B7qfzNKteUEOTsZFFnBW1cqHNVb6NLpID2qPd5VozlPs/qffHtjaF7MqjfD4b/VfZv2eFOnRt9oYd3TQleqVzr5lfofbWdxR8Vwq9q/7a4UtolX4nHtVZXnbe5Y54IJlVppHOIlafVCf0lZbUOL1beRqWkXNtZkNq7XhuybW+SNnmheIijG4rheiBDG63KaE9bJwZ2xRo6qkMZrVUTtddW3ecBISLspTy9VRrpe72kQNr49rrOMeiSVyr3H7RnUwlAhccR6O/HrCcvJq/jQzK2073UrRLKPFcTmpV+j/8ds2hQVVK+vs1oygeh9xITLfOn0ct3Mc3Vksj4RtSLCaXUZTNlnXxGN3ROAGDlrkzmb9WLUcrYnbw21d031UktW70ri3t/WsY1XyzwKgF50qjWQjzBIPeXCpdW9dJX/64XD5sO1qJWSggM5LfrjYZaLdxwRE7E2PWxbdtDf5x9J3g+w7lEUT586fw4Xz9O6vqdw7w1SWT0H+knITthQf58e6MIh/yxbBerkkXsQoUwd64biY/P+VlyyWAwnEX08BBh6fuC3o5pKgZcUT5k7hAvjwpvVmI8qiQOaGNw6wyZ5O/fqkM/c1Lhw066HA+IgXmwOM7RUHzA24hOXiLqtupc6Zvhg3bwig6RdBvlarLvGTa4bZZ3aHZBlnhZig+A5Ux50tZpL5kyVNQ5qjTWxuumfyCkikxwwzz+J9Zo4x1qmzhU2mot4JEtZb1Bhn8PHx+4baaoOzcb5r3vsk9ElKlSTWhzk+5vf7u0SjnbrYLtfKbKeInvAHGOoJiqjRzTRBuWC528T/diyBrxAAdHaQN8kTMmtrlcx971WhTOXUfbWWyKrq9Fl1TZpJgmkle7Y54YqbHNtSdaGdsJXXTOeF4aVHHEmhIu0O+5Zif5W4XG6D7Psj4gOa0gxl1IFTHq1f7Bn0ue6+3iTY4MDSSk31Nw1S8QEMytF9QG4JJWCVg+PrSO17olDauGYVkWUaGBZOYXAzKnUhoo38+Xco03O+dYvyfHbeB2rB1JRn4xLpfNoiRdAmrJdtFZ+Wi61iEYuVjnOLtcNsM+nse1Xywg18MLfFz0fV7ur3YeYoF1ekq7+CsYfYdsd39cfw4qpxvg+8HiXR9zn7foneGQnIhxOsFRYr7esqzrgbGA0c02CN9fLmErA9/XcuznMKOXi6jItR31BKpr/Wh3Xu4l788mK7+YF8dJaMqTFzUuexKDwWA404hrKyIs146GTnfrfhVi+7PjvarbS3LbQEJ2bVv+B4THSzjlTsczoVRnQYvxTH5aRHtmeWhcfjVADFKlXFpcAF9dCJOe1mOW/wTvtdEKsrl74YWq8KbH7+vnPSWncPIIee5ZE3X/NjHWFcpg9cq1naw92MpTtW+TeNMSukjoaPoG7fFShqqqQxzbXHvuQDy9oA0MEEEby4KHt8Ddi7VBZDizaDII/rcP7lvpLWCU0FmHEDe/UpSdQcZU8lhUaXKZzu10C5H11OHOSkgqJlHChNM3iEEaWVcvmKj6vtVa6tza5MViTIY5hqf6jlVtphegMpw0Ar9AyavFCduv0UZ/bxWVa2lDFbQycaJHWL9SHO72iHy/lTKxZclvRc+ntKJ4UDg8vAlumaL/bn6BspigwsQPYljbODY+P4AXnLSw7g10JNzg1jUAaBSrr7t9rUia15BzrXUM22rhEk24bGcG61NyqBIWSNPqEkKdkl1AicumVXwlZ4wshs3z8ABPWK3zpz+cvpmFSfuZtSmdx0d5KFY7JKXncaDoBCJV/AIkVP2ANsCJqqsXwdQChm17p1AoITXDYTkRgaqHgU+BZkBz4FPbtv97si7McBYz4zVZIUwcCq2uPd1Xc8ws3LafXm9Mp+cb0+n31kwSHh3LjI17Dzm+1GWzP6+IuIgKBPr5eu377iYtb9/82YlMXZ9G70YxNIqtePBpDAaD4cwkcYjO41Uor5fy4rZzhFaqNJFJugqbbDwQImqJ+BJo4RrQ4jueJTcyd4rnVJV3WeqUcln2neQOz3lbG6ijbxfDUxmyS76WNjcFsnZ55+GqUGtPBdrtc7Q3DiA/3THSc8RIB/GaKUO2zY3SbpwgbVC4LiuizhsUrkWAQAwOT8NBhYxe+Cp0vBtumqz3hURpRWDDmcnBpbwUd8wRhfPLPvbuV0Zg5VpSkxe8xeDq9vJeDPHxE2NZeXJBwoOjteowIIau8pKWFkpYMehcXpCcZOUZVMeADiUGqOfUZW7oCD1F1pX3GBIlIbRB4dDjMdnX6BLxbg//WxZoQOrOProduj6sz1mnh6hR+3jPh46VAD8fr+0PrmrFExc2YkBTUWK+tav8PatWDCIk0I9OdXXudc3IYCzLIi6iAjv3HyC3sISm1cOpW0WUpkctFa9tpzpRhFfwJyk9zz2XaxATRpC/D9v3a6/p13OT3NsqFU3xxextdH99Oo3+N4FSl81xM/Rrvd3fiTLx8ZXSR8p7r0pZKS/7Zo/fD8MhOaGwY9u2f7dt+wHbtu+3bfuPIx9hOOfZuQimPS8hWpd9crqv5pgoKXVxz0/LGPbJPLbszWPr3jz25RUBMPzLhTzy24pyjxu5WHLZ/tMmrtz92166kMtbVXc/f31os3LHGQwGw1lDtZYygQeZjKnyGcoLpNSbG10iSsIAGdvFKKxYQ4R5lPKs8jqB5M3u9yhppESdVD4tiJJsqUcoYapjjHoa0pv+0arIoD1pniHJ22bpOqHua0yS0M34DvKe9q73KLXiGPgLnRDU2BbacEnfqMuNeJa5qd5K8j0bXQL+wXpRoEJl6PfCeRH5dF7g41tW4Rzknnlsl3fZmq4PSf5vo0vEaPQL0AskSvzNc3Gp0aVO6LtHjVf/IJ3PCfo+VDm5IGHMAcF64UapgLe9RUKs6/WV/FyQkOKL3hAhUcV/vodHd2gjHSRX2VM47RRyUbNYbulaG8tZcLigXjQLn+jF9Ie7AxAc4EegYyAPdby/TauFu49vWq0irWvKZ/T6RPkdaB5XiabVK5J1oFiUnIHuDaPpWi+agmIXhSWlFJW4SM8tIqZiIC3jK1FU4vIStHp+7Fr39u9LPMo7AVPXp/LS+HVkOHPJwxLXTu6TO+ZBhzt0f0Jn8fAXZMGU56RPGcOqZrMnRsyqDH7HeoBlWbNt2+5iWVYO7jgI2QXYtm0bl9X5SvEB+NYJbbl+zAmv6p1KSl02F707mw2pOXSpG8W7V7YkIiQAgMVJ+xny8Tx+XZxM57pRXNqiutexPyzYAcB1nRLKPbdlWbw5rAVvDmtR7n6DwWA4K7lzHqRv0kI1IGVdVv2qcxKrt9bCTCrvLLK2GMhbpkitWZAwzZ0LJCS52CO0WBmpyrMB4hEO9RD4y9ktHlnP8OPdy8W4BGmL8yVvN2ObXNOuJXJO25kYJg4RL68KCwyJknDS7bMljDmwohioIGq4IEZtRG0Y73i1lKHf7REJSa3SWNR6QQwHw/lJYKj38zr/z955x8dRnP//M+pdsty7MLZxww1jbDoYsLHpJLRAEnoSSCDwIxiSAMmXloRACiV0cEIJEIoxprgbjHu3ZUuWZdmSLatYvV+Z3x+zc7e3t7s3ezrpJPl5v1566W5vdnZ2d3Z2nnna+SJdlJ6fLgL2LARmasGLJl4rLBViE4Ahmkb2/N8BS/8AnHW/+J41TETzLd0GzPyF2HbBY8JnXa+9vWudMNGXJrFxCcAdKwKPn5AitLTdjH7pgYFPv/3NedhX3oCZI4SWd/b4AfhSM0f+wSlDMTQ7OaD8GSN749t9FVhTcAx/Xypir8w4oTfSE+PwTW4ZthfXiry8AH54ylCkJcVh66EaLN1ThssnD0be0XpwDswYkY11hVX4eGsJrjlVLHrVt7hwy1vCwmVpbhmW3X9u6BPSLyxIRl0kLGi2LPCPPQMmCCua8t1i7JMWB/+7TWh/b/rE7wdMONfscs7P1P6nc84zdH/pJOge53x4s8jVdtnzIql9N+K2tzcir6we108fiv/cdppP0AWAaTnZuO/C0QCAe97fhqJKv2mL2+PFzsO1SIiLQUZSfKe3myAIImrEJwMDJwamlNOb7GYNF8Ke1HR+oU3Sx14mzCUBf3CoKTeK/2W7RHRjwO8X2FgptL0yZ2fZLr+ZtNQu1x8VJs3Sd7I81y8g69OtAEJIyBgsUshIYVrmXl39F/F/yKnAcC21SsNRIFNoinzBegChHUsf4E8VJNOIDD9daMR+ZohsTRBWDJ0uAhdJv9uYWODWb4QQLJlyo/B9nfEz/7bLnxdBnqTfbXwyMO8Z4CRdBOSE1OBcwz2UfhlJOGNkH18A0MsmDcJF4/rjqimDMUwzbZbzO8aENvg8zR9YCsVnjuqDGZqwvHRPGT6Q1nunDsWlk0RguXWFwrdWRnF+YPZJSIiNwcaial9bXtQFu9pf0QivzsS51e3Br97b6hOwbZn8I/H/G80kPk4T2EecK/5LC5aaYr+Z839/HLre44j25Nn9t8o2kzJvMMbKGWO7dNuyGWNLGGP7tP+9tO2MMfYPxlgBY2wHY+z4eFq7Izs+BPK/FOHwu5mf7iur92NFXgWmn5CNp64yNzH+1axRSIgVj8u5z6yE2yO0AZ/vEBqLSyf27GjTBEEQSsQlCu0SAFyjpRtKHxBYZsw8f+Cc77Rcs0NPE6lNKvb6U/KMOFf8P6gJjQMmiIleRZ4/KJXURskyfUcLM+myXL+md6wmNMv0R/3G+7XRB9cIYVW20Suiu2LwVH96F8AfWXfyDeJ/YqY/nd6dq4G7NwNjL/GXT8rs0XnlCaI7EBPD8MqPp+HZa/2WdbeeKRbI7rtAKDFOG+E3P0+Kj0F8bAymaNGfV+VV4LsCkXN3aHYKBmcJQXOJlrbo5dX7kZoQi6nDemFaTi94vBytbhGo6tt9FQHHW7rHr82/8bX1WLj9CJ5bmo/Ptx+xP4nkrMCx6F7NXUOmhcvTYgN/81t/mbb6QFeP45z2jMTj9V8YY3EATlHY7y0Acwzb5gNYxjkfBWCZ9h0ALgYwSvu7A8BL7Wgv0VHUHgY+vk2YpV2zINqtccT+igY8uXgvkuJj8M5tp9mWzX/Cr7EY+VsRIfT9DWLF7+G5Y0z3IQiCOO4489citYbMKQr4g+PEp4hUdDK6bLOmCekzWvw1VgiNbNYwf+7J754T/wdMFAJv3WGRxxbwa2TXPq+VOVkIvK5GITTHp/rbIQNLDZnmF7YBoeUF/NpmQGilh5/uTy80XROqT78buPlL4P69/rJJmSJyKkEQXZ67zhuJwifn4pezRDC4lIQ4jNICV82fI+ZysTEM6YlxyCurh8vDfZk1ABHlubKhFW1uL2qaXOiVmgDGmE8bvGJvBTjn2HW4DlOHZeH66cKsWUZ6FmmPzDXAgEiDdNe7W3wCNQDgxo/FIuJdG/wuHFIALlwl/ud+Jv7LPL76qM3HOY6FXcbYQ5q/7kTGWJ32Vw+gDMBnofbnnK8GUGXYfDkAmXH+bQBX6LYv4IJ1ALIYYwNBdC3+faX4/8O3RCCObgLnHLe+JdJh/PeOmYiPDf045D/uF3hz5n+B9QeqcNOM4eidlmizF0EQxHHO1a8Bk24ArntXfNf73Kb2FTaFUtvKPSL4kxQ+j2jBfYZM8+cNPbpTBOyRJsSyzKApYl9A5KLsO1poWGN0biZDTgVGzfZ/H6lFrJVa4iHTRXsYA+7PF+bIesF9+OnCx5EgiG6JNHOWfP7LM7H4V2fhp2ec4Nt26WS/xZ50ZQOA88eKsWv+/4SG9copYrFs3kQhnqzdX4mVeUKre+G4ATixrxCkNxwQos/3+4XQK02iZQ5gQMxLr37pe3yxoxS3L9jkN32OiRGLiDL6OyDm23FJIhZCuW7xbZwmQunzhQOiTL3BV/w4IRyf3ac45+kA/mLw1+3NOX8ozHb055yXavWXApBvwcEAinXlSrRtRFdh9TMizUTOWYEmXN2AF1fuR9GxJtx25gmYNNQ815uRhLgY7HjsIt/32BiG/zf7JJs9CIIgCGSPAK58KTDK7My7ATDgoifEd5mjFhB5aGNihGmzZOhp/nQrgNAEMwak6cykh87w+/oCfgFaf9zENCEEnzhLTBZPuVlsn/Fz4N6dwC1f+cum9RXaZIIgeixJ8bEYNyhQWfPIJeNw/fSheOLKCT6zZgC47lQR3frjrcLf/7YzRVCpEX1SAQBL95T7UhVdPGEAGGMYlJmE3UeEUCvNlu8670TMHi98tOtbhPvEQoNJs/QJlqzKr8CCtUVoc2uB9WSO5hc1S5lZj/gX+/Sa3cObRZm/jvZb0xxHhKPZlfaaHzLGphr/Itw+s4RmQUmsGGN3MMY2McY2VVRY50MlIkz5XmC5FgZdrtZ3E/ZXNOAvX+ehX3oi5l/szAQ5IykeRU/Pw4Gn5mL/k3ORmUyBqQiCIBwz+wngsRpg0rXi+7DThBlyn9F+AVTmCU3uJYJd6QXZCVeJ/yPO8W9LTAMG6SLfS5O+S54TuURv+tT/200fi6i4+pQxWcO6VSYBgiA6hqT4WDx11UT86LThAdsnDM70xXA5e3RfZKaIOSBjDAMyknC4phmr8oUskqMJwGMHCkG6qrENK/OFADtmQAamnyAW89YUCG3v88sLAADv3zEDQKCf757SOvzkjQ145LPdeOQzLa7BeQ8HNvrM+0Se5pTegenaPvul//P67pUWNBI4Tj0E4D4I/9m/mvzGAYQT67qMMTaQc16qmSnLpYwSAPrkpUMABHlyc85fAfAKAEybNq0dGZ0JZbwe4E3NpPfad7qd+fL1r6wDALx041TEKZgvm8GskssTBEEQ4fGz7wK/X/68EGZlHtGEVODyF0Tk0SlaxNG5z4hoySfN9e/32zLA6/anfckcAlz3Tse3nyCIHs/WRy7E1kM1AcGtAOCSiQPx2ncib/jpJ/qtUmaN7Y9le8vx4aZilNW14tyT+gIAzhndF/8HYeJ80bj+2FfeAAAB0aAlv//UF9cX728sxtNXTxQuFZOuB7a/h6PnPYeUVrfIDJIxGGg6JnLuMiZSFEmKDGPscUA4Zsx3aP/PM/kLN6nTQgA/0T7/BH7f34UAfqxFZZ4BoFaaOxNRZuGvgOYqkUKim5kvP7ZwN8rrW/HT03N8CcYJgiCILkhcoki5ku33pcOUG4HzHvJHO07KEOZ7ehPn+KTg/KYEQRARIDUxDmeO6hMU6+WeC0ZhUKaI0v7Xayb5tl+kmSs/9aXwrZ01RnhrnthXaH5X76vA3qP1AOALaNUnLQGVDW2+OjYdFObHZ44UwbLqNNNnXPkv/OOsTZjxZX9MfOwbEQ163GXit31fAzs/Ep/HXwUkZQFF37b/AnQz2pN66IeMsXTt8+8YYx8zxqYo7PcegLUATmKMlTDGbgXwNIALGWP7AFyofQeAxQAKARQAeBXAL8JtLxFB9i4Gtv1HBPz4wRvRbo0jVuaV4+21B5HTOwW/v2RctJtDEARBEARB9ADSk+Lx/UOzUPT0PAzMTPZt72MIYnqt5vfLGENaYhwKyhvw0eYSAMD108VvMv9vdWMbthfXABCa4wu0AFlfa3mBOed4dkm+r+631hQBYy4VX7YsENlSAODch4SbCAC0Nfkb4/UCuQsBj6udZ991aU/qod9zzusZY2cCmA0RRflfoXbinF/POR/IOY/nnA/hnL/OOT/GOZ/FOR+l/a/SynLO+V2c8xM55ydzzimOdrSpLQHev158vmOl8KHqJny/vxI/fVNEX/7gzpmIjSEzZIIgCIIgCKJjefPmU3HaCdl49NJxSIjzi1/njBYmzW+sEebPJw/OBABMHiYCp35XUIlvcoVge/MZObj4ZBH1ecshoeldr0V5nqmZPi/bW+6P2ixz8ALY0NAHGK1lfj30va5hFwMf3AQ8f2rEzrWr0R5h16P9nwfgJc75ZwAS2t8kosvibgOeny4+z36yW0WoXLTjCG54dT0A4M2fnop+GUlRbhFBEARBEARxPHDeSf3w3ztn4mZdeiNAaGsll00a5IsHc75m6rxsTxkW7ShFQmwMpg7rhX7pQkucXyb8ez/YJJLWPDx3LNIT40SKI8aAsx/w1bvAfSFueHWdiHIPAMVC8YO2RqBYxLBB9QERj6cH0h5h9zBj7GUA1wBYzBhLbGd9RFeGc+CVcwBXIzD6YmDmXdFukRKtbg+ue2Ut7n5X5GB87tpJOG9MvxB7EQRBEARBEETHMmfCAEwemoX+GYn4zRx/KktpBr1oRykOHmvCtJxeYIyBMYac3inYrPnwLt4pQhmdPCQTowekAwCa2zzAuQ9jfcZs/M19FR5x3wy3l6MmfZSofPcnAIC6r58MbEzup+iJtEc4vQbA1wDmcM5rAGQDeMB+F6JbwrkwcyjPBXrlANe/F+0WhWTX4VqM/u2XOOl3X2FdoTDxWHj3GbhyypAot4wgCIIgCIIghN/up3edgfUPX4AhvVICfhuanQy3VySZuWaaPznN4F5CEC6tbUaLy4sZWlToC8eJQFg7D9cCMTG4vuIn+Jv7B3jySuGr+0VuJRCbAFTm4bt9lcjY/DwAYO9VS0TFh9Z13IlGkXBSDwEAOOdNjLH9AGYzxmYD+JZz/k3kmkZ0CdxtwMtnARV7gcRM4K4Nwjyig+Cco7HNg9wjddheXIPc0jrkl9WjpsmFo3Ut8Hg5YhjgdZBgasLgDHz6izPCTjFEEARBEARBEJ3J7+eNwx3/3gxAmDhLLp4wEGsKjuGe97cBAG6akQMAOGV4LwDApoNVGNE3FV4OTBqahbknD8DDn+zE1kM1+NHoOcCehXj/vTehGTXjL5uB1wGgpGeGRgpb2GWM3QPgdgAfa5v+wxh7hXP+z4i0jIg+daXAv84QubqyhgF3bxJpICJETVMbnl9e4MtJpkooQTeGibDwN59xAu6dNQoxFIiKIAiCIAiC6EZcNH4Avr73bAzKSgqYy8qgVhu04FTSv3fKUBHUau3+Y4jRFFM/njEcWSkipNLKvArghtuAPQvxvPdxAMBHnrPxbcExoO9w4MgWAMDeo3V44os9uGfWKEzL6f4pOsMWdgHcCuA0znkjADDG/gSRUoiE3Z7AtneBT38uPk+6AbjixXZrdF0eL/62NB8vrNivVD4+luHkwZnI6ZOKE/umoXdqAhLiYhAfG4OUhFgkJ8QiJSEOAzKSkJUSj8S4GJ9jP0EQBEEQBEF0Z07S/HD1DM32mzuf0CcVyQmxAOCzYNx8sBrJ8WLbPC0AVp+0RFQ3tQEjLgio66PBD6KtqBbe7BGIqTkINFdjzt9EtOZLJg487oVdBn9EZmifSdLo7jRUAB/fDhSuABLSgeveAUac064qP9t22GdqYcbfrp2MORMGIEl7MAmCIAiCIAiCMGfJr8/GqvwK/OCUwFg003OysaGoCt/klmHqsCzf3PrMkb3x6bYjaGh149bsDzG7/HVc86s/Yfq2VqwrqkVJv/MxrHAFCtYvBpCFEX1SffmAuzvtEXbfBLCeMfaJ9v0KaCbfRDdl72Lgo1sAdzMw6XrgkueA+OTQ+5ng9nhxy9ubsDq/Iui3D+6ciVO1qHIEQRAEQRAEQagzqn86RvUP1vqeN6YfNhQFmjcDwOShWfh02xGsKahEbhXH7thbcEu/HJw9ugr/WF6Ar9sm4HYA1Rv/i2HsCixv+DWw6Tlg2i2ddUodRnsCVD3LGFsJ4EwIje7NnPOtkWoY0cms+guw4nEgKQu44b9ha3Pb3F6c8aflqKhvDdj+yk2n4KLxAyLRUoIgCIIgCIIgDNx21gn4bNthVDa04razRvi2zxrbH499novnluSjvsWNX5x7IgBgwuBMAMC3lWm4HcCpjauwOnGV2KnuSGc3v0NwLOwyxpIA/AzASAA7AbzIOXdHumFEJ7LoPmDT60D/CcBPPgdSnNvnuz1ezP3Ht74k15LNv7sAvdMiF9SKIAiCIAiCIIhg4mNj8NW9Zwdtl36+e4/WAwAumSiiOyfFxyI1IRa5R2rhGno64ou/9+903m87vsGdQDia3bcBuAB8C+BiAGMB3BvJRhGdyNI/CEG333jgjlVArLMu4fFy3P3uFny566hvG2PAjkcvQnpSfKRbSxAEQRAEQRCEQyYOycSOkloAwLhBGb7tI/qmYefhWnw4eD5uKL5MbMw5q0NTjXYm4Qi74zjnJwMAY+x1ABsi2ySi01j3EvDds0DfMcDtyx0Jupxz3P3eVnyxozRg+/ZHL0JmMgm5BEEQBEEQBNFVeP+OGVi0oxRnjuwTsH3GiGzsPFyLh1c24EX2N6y8cwzicmZGqZWRJxxh1yU/cM7dFGSo+7GjpAaF33+CK3Lnoza+Hx7P/gembCnHnAkDkJ2aYLtvbZMLZ/15OepaAi3X1z88C/0zkjqy2QRBEARBEARBhEFKQhyumTY0aPsVUwbj1W8PAAAG5fQsQRcIT9idxBir0z4zAMnadwaAc84zrHcloklRZSN+++lO5BcUYGPSvfCC4arG32D/9kp8uL0SD3+yE+mJcZg8LAtjBqQjOT4WqYlx2HKoGl/vLjOtc8388zE4K7yIzQRBEARBEARBRPHFC8wAACAASURBVI/xgzIxbmAGckvrcN+Fo6PdnIjDOOfRbkNEmTZtGt+0aVO0m9Hl+HJnKe55fxtcHje2pd+HTFc5cM0CYNzlaHF5sOVgNb4tqMSHm4pR2dBmW1dmcjy+e/A88sklCIIgCIIgiG4O5xweL0dcbEy0m6IMY2wz53xaqHLtybNLdBM+2FSM33y0A+lJcVg3YREy88qB6XcC4y4HICKxnT6yD04f2QcPzhmD+hYXvt1XiTe+O4CqxjakJcXhJzNzcMmkgUiMi43y2RAEQRAEQRAEESkYY4iL7ZmuqSTs9nD+t7kEv/loBzKS4rBiVgmyl70PDJwMzHnacp/0pHjMPXkg5p48sBNbShAEQRAEQRAEETm6j666J+FxAxteBRoqOvQwH24qxv0fbkd2agJWX1KP3svuAxIzgR9/BsTQrScIgiAIgiAIoudCEk80+PAnwOL/BzwzEqg/Grp8GHy69TAe+GgHMpPjsfS8YmQtulX8cPsyIDmrQ45JEARBEARBEATRVegWZsyMsTkA/g4gFsBrnHNrG9wuTn2LC+l7F/k3/PUk8X/MJUBCGgAO9B4FlGwEYuKAURcCo+cA6QOUkzt/vKUE932wHRnxHOuG/BPJS1eLH368EOgzKrInRBAEQRAEQRAE0QXp8sIuYywWwAsALgRQAmAjY2wh5zw3ui0Lj5++uRH/k19GXgAUrhJCrF4A1pP3hXVl/cYDp94KjJkHpPUHGMMbq/Kw9OtPsSXpeWSjDijWyt6xChg0OYJnQhAEQRAEQRAE0XXp8sIugOkACjjnhQDAGHsfwOUAuqWwu+dgKZAEHPL2Rfyl/8HATC1HLeeAqwloKAda64CqQuDIVqCywFrgLd8NfHGf+NO4BcAtCboyfU4Cfr4GiKU0QQRBEARBEARBHD90B2F3MPz6SUBod0+LUlvazecJvwUArPeORe7qQjx66XjxA2NAQiqQfYL4PnASMP5K80q8XqD6ALDlbSB3ofiswzP2csQOOQU4/VfKps8EQRAEQRAEQRA9ie4g7JpJazygAGN3ALgDAIYNG9YZbQqboQP6AuWleCv1ZuxeU4Q31xTh+ulD8eil45EUr5jDNiYGPHsElg2+C68UXowNLVUAgD9ePh4/npkDyoRLEARBEARBEMTxDuOchy4VRRhjMwE8xjmfrX1/CAA450+ZlZ82bRrftGlTJ7bQIbWHgfJc1A45F7P+ugqVDa22xc8a1Qd1LW6c2CcVJw/JxJZDNdhXVo+9R+t9ZS6ZOBCPXDoO/dKTOrr1BEEQBEEQBEEQUYUxtplzPi1kuW4g7MYByAcwC8BhABsB3MA5321WvssLuwYKyutxwbOrHe/HGDBxcCbeunk6eqUmhN6BIAiCIAiCIAiiB6Aq7HZ5M2bOuZsxdjeAryFSD71hJeh2R0b2S0fR0/MCtnm9HPWtbhSU16OwohFNbR4MzEzCmAEZSEmMRe/UBDDyxSUIgiAIgiAIgrCkywu7AMA5XwxgcbTb0VnExDBkJsfjlOHZOGV4drSbQxAEQRAEQRAE0e2IiXYDCIIgCIIgCIIgCCLSkLBLEARBEARBEARB9DhI2CUIgiAIgiAIgiB6HCTsEgRBEARBEARBED0OEnYJgiAIgiAIgiCIHgcJuwRBEARBEARBEESPg3HOo92GiMIYqwBwMNrtCEEfAJXRbgTR46B+RXQU1LeIjoD6FdERUL8iOgrqW12L4ZzzvqEK9ThhtzvAGNvEOZ8W7XYQPQvqV0RHQX2L6AioXxEdAfUroqOgvtU9ITNmgiAIgiAIgiAIosdBwi5BEARBEARBEATR4yBhNzq8Eu0GED0S6ldER0F9i+gIqF8RHQH1K6KjoL7VDSGfXYIgCIIgCIIgCKLHQZpdgiAIgiAIgiAIosdBwm4nwxibwxjLY4wVMMbmR7s9RNeGMTaUMbaCMbaHMbabMXaPtj2bMbaEMbZP+99L284YY//Q+tcOxthUXV0/0crvY4z9JFrnRHQdGGOxjLGtjLFF2vcTGGPrtT7yX8ZYgrY9UfteoP2eo6vjIW17HmNsdnTOhOgqMMayGGMfMcb2auPWTBqviPbCGPu19g7cxRh7jzGWROMVEQ6MsTcYY+WMsV26bREboxhjpzDGdmr7/IMxxjr3DAkjJOx2IoyxWAAvALgYwDgA1zPGxkW3VUQXxw3gfs75WAAzANyl9Zn5AJZxzkcBWKZ9B0TfGqX93QHgJUAM5AAeBXAagOkAHpWDOXFccw+APbrvfwLwnNavqgHcqm2/FUA153wkgOe0ctD64nUAxgOYA+BFbZwjjl/+DuArzvkYAJMg+heNV0TYMMYGA/gVgGmc8wkAYiHGHRqviHB4C+L+64nkGPWSVlbuZzwW0cmQsNu5TAdQwDkv5Jy3AXgfwOVRbhPRheGcl3LOt2if6yEmjoMh+s3bWrG3AVyhfb4cwAIuWAcgizE2EMBsAEs451Wc82oAS0AD8HENY2wIgHkAXtO+MwDnA/hIK2LsV7K/fQRgllb+cgDvc85bOecHABRAjHPEcQhjLAPA2QBeBwDOeRvnvAY0XhHtJw5AMmMsDkAKgFLQeEWEAed8NYAqw+aIjFHabxmc87VcBEVaoKuLiBIk7HYugwEU676XaNsIIiSaKdYUAOsB9OeclwJCIAbQTytm1ceo7xFG/gbgNwC82vfeAGo4527tu76P+PqP9nutVp76FaFnBIAKAG9q5vGvMcZSQeMV0Q4454cBPAPgEISQWwtgM2i8IiJHpMaowdpn43YiipCw27mY2e1TOGwiJIyxNAD/A3Av57zOrqjJNm6znTgOYYxdAqCcc75Zv9mkKA/xG/UrQk8cgKkAXuKcTwHQCL85oBnUr4iQaOahlwM4AcAgAKkQ5qVGaLwiIo3TvkR9rAtCwm7nUgJgqO77EABHotQWopvAGIuHEHTf4Zx/rG0u08xloP0v17Zb9THqe4SeMwBcxhgrgnCnOB9C05ulmQkCgX3E13+03zMhzMCoXxF6SgCUcM7Xa98/ghB+abwi2sMFAA5wzis45y4AHwM4HTReEZEjUmNUifbZuJ2IIiTsdi4bAYzSIggmQARKWBjlNhFdGM3P6HUAezjnz+p+WghARv/7CYDPdNt/rEUQnAGgVjPJ+RrARYyxXtoq+UXaNuI4hHP+EOd8COc8B2IcWs45/xGAFQB+oBUz9ivZ336glefa9uu06KcnQATj2NBJp0F0MTjnRwEUM8ZO0jbNApALGq+I9nEIwAzGWIr2TpT9isYrIlJEZIzSfqtnjM3Q+uqPdXURUSIudBEiUnDO3YyxuyEeklgAb3DOd0e5WUTX5gwANwHYyRjbpm17GMDTAD5gjN0KMRH4ofbbYgBzIQJvNAG4GQA451WMsf+DWHABgD9yzo0BGgjiQQDvM8YeB7AVWqAh7f+/GWMFEBqS6wCAc76bMfYBxMTTDeAuzrmn85tNdCF+CeAdbUG3EGIMigGNV0SYcM7XM8Y+ArAFYpzZCuAVAF+AxivCIYyx9wCcC6APY6wEIqpyJOdUP4eI+JwM4Evtj4giTCx2EQRBEARBEARBEETPgcyYCYIgCIIgCIIgiB4HCbsEQRAEQRAEQRBEj4OEXYIgCIIgCIIgCKLHQcIuQRAEQRAEQRAE0eMgYZcgCIIgCIIgCILocZCwSxAEQRAEQRAEQfQ4SNglCIIgCIIgCIIgehwk7BIEQRAEQRAEQRA9DhJ2CYIgCIIgCIIgiB4HCbsEQRAEQRAEQRBEj4OEXYIgCIIgCIIgCKLHQcIuQRAEQRAEQRAE0eMgYZcgCIIgCIIgCILocZCwSxAEQRAEQRAEQfQ4SNglCIIgCIIgCIIgehwk7BIEQRAEQRAEQRA9DhJ2CYIgCIIgCIIgiB4HCbsEQRAEQRAEQRBEj4OEXYIgCIIgCIIgCKLHQcIuQRAEQRAEQRAE0eMgYZcgCIIgCIIgCILocZCwSxAEQRAEQRAEQfQ44qLdgEjTp08fnpOTE+1mEARBEARBEARBEB3A5s2bKznnfUOV63HCbk5ODjZt2hTtZhAEQRAEQRAEQRAdAGPsoEo5MmMmCIIgCIIgCIIgehwk7BIEQRAEQRAEQRA9DhJ2CYIgCIIgCIIgiB4HCbsEQRAEQRAEQRBEj4OEXYIgCIIgCIIgCKLHQcIuQRAEQRBElDha24IWl8f33evlKK9viWKLeiYtLg/K6+i6hkOr2xO6EEF0UXpc6qHuSEV9K2IY0DstMdpN6ZI8tnA3fnp6DnL6pEa1HfUtLhyuacaYARkdepyK+lb0SUsAY6xDj9ORrCmoxH/WHcRLN54S7aZ0GsVVTRiUlYzYmI69by0uDyobWjGkV4pS+aW5ZRjVPw3De4d+flrdHrS4vMhMjm9vM6MG5zziz055XQvWH6jCpZMGKZXfVlyDtMRYjOyXHrLsd/sqMWZgOvrI8b9gKZAxBOg3Jqy2trg8SIqPDdr+/oZDOGNkHwzNTgkqHx8bo9xvj9a2YEdJDS4aPyCs9hHBzHhqGeaMH4B/3STGy3+t3o8/f5WH9Q/PQv+MJADA6vwKDOmVjBF90yJ67D2ldYiNYRjdP3Rf1VPX4kJJVTPGDQr/fVhXV4fy8nK4XC54OUeM4bnlHKhvdSE1IS6of3q9HBxwNN5WNbahqc2DIb2Sw26zHrfXC86B+Fg1vZHb40Wbx4uUBOupt5dzlNe1IjMlHskmz3Gk4ZyDcyBGdx2b2tyIi4lBQpw4r1aXBxUNbeibnojEuPbpyLxeDi/niFO8Zk7waHWr3g87vJyjze01HUvtcHu9iItxdvxmlwdJcTEdPufjnANAt5hbxsfHo1+/fsjIiNB8W3T0rvsHIAnABgDbAewG8Ae78qeccgrvbgx/cBE/Yf6iaDejSzL8wUW+v2hz54JNfPiDi7jb41Xe5/ef7uRLdh9VLl9Y0cCHP7iIv/ldofI+lfUt/IEPt/HmNrfyPpxzXtPYxq96cQ3feqja0X6hKK5q9N2zPaW1Ea27q3LomDjn55fvc7xvc5ub//LdLTz/aJ1S+eteXuvoeRj+4CI+4qEvlMpe/vx3juredqiaf7ylWLl8SXUTf2rxHu5RfIaa29z8F+9s5rsO1yiVr2lq41P++A1fuO1wyLIV9S3870vzeavLE7LslD9+w4c/uIg3tLiU2uFkzBr+4CJ+wV9Xii+Nxzh/NIPzp4ZyzsWz9MHGQ0H7uNwevmJvWdD2nSU1fPiDi4LGnOrGVj78wUX8xtfWmR7/1//dGrT9w03FfPme4GOc/tSydo/HT3yRy//fB9vaVYcqb35XyIc/uCjkffZ6A/vkxgPHeGV9i+0+X+0q5fvK/M9tXXMb/3LnEdt9Kutb+KdbSwK2GfvL7OdW8eEPLuK5R2oDypzz5+W2deceqeV1zW2+721uDy+rbbbdJ9z3q9X7sMXl5lsOVoXcv7a2lufn5/PGxkZ+sLKB7ywJfsYbWlx8e3E1P3isMei3fWX1pvt4vV7e0OLiHm/wGLO9uJpvLzZ/3zW0uHibO7iP1De38e3F1bzF5P1qV19jqytonNtVUhNUvqnVzRtbXQHftxdXK78P2svBY418R3HgddxeXB3Q947UNPHtxdUBfamxxcWPNbTa1u31enlLmzvg2dpzpNbymun3C/WO8Hi8/EhNU0C5fWV1IetW5XC1OGfjvMrr9fJdJTWm536soZVvL65Wfk9wznltk+hfpSbPaVNr6GtspE7rr2Z9edfhmqB73RXxer28sbGR5+fn89pa+zkkgE1cQZbsDmbMrQDO55xPAjAZwBzG2IwotyliVDW2AQC8PMoN6YJsLKqKdhMC+Gr3UQD+1bFQuDxeLFh7ELct2KR8jE+2lAAAFqxVypMNALjr3S34YFMJPt16WHkfQFzfzQer8a+V+x3tZ8enWw/jzD+t8H1vc3sjVndX5ra3xT1+f+Mhx/tuOFCFhduP4G9L9ymVX1t4TLluOb54FAeYbcU1ynUDwOUvrMGv/7tdufy1L6/Fv1btx87Dtcrt+WJHKZ5bkq9UPr+sHlWNbXj128KQZf+5bB+eXZKPNQWVIcvK62jUPLUXabq6r7xBbHA1aT+I63Pty+vwwEc7gkwI/7G8AD99cyO+3VcRsF3ev2V7ywO2t3nEc5h7pM60HR9vCR47/t+H23HzWxuDth+uaTato9XtwdLcsqDts59bjeteWRuw7ZXVhfhwc4nve4vLg5lPLcMnW/3bvt1Xgbvf3WJ6LElzmwcF8tppjP7dl3hxZYHv+9+XieeqodUdsJ9+HP/32iKc8vhS33jl9XL84F9rcd8H/r59oLIRrxn61Z3/3owLnl3t+37fB9vxs/9sQWFFYJv0/OHzXNzz/jaUVDdZlpHPq7G/FR2z3gcALv77t7jp9Q2+7498tgvTn1ym/PwD4v4uWFsUcH3uWLAJTy7eE1BOvg+9hvfhy6sKceWL32OX4RnnnCNn/hd4+su9AIDy8nIMHjwYKSkpqGl2BdUj9wHEu9RIU5vbdJ+GVjf2VzT4nllV9lc0IP9ofdD26iYXAKCxTd2Mt8Ul+mV5fWvAdo9Je/eV1xv6cHCZ6sY2HKry33vOOUprmtGsa5PL48WxhtaA+9bc5kabYewwzl9qmtrATY5pds2h644HKhuD+nBpbTPqW1y+77XNLuSV1Qc8e20m9ba6PQF9tKK+FbuO1MKtK3ukphllOhP0ysZWVNS3oqLBf42b2oLPdUdJTYBLQIvLg6O1LQHX4dCxRhwyPFuyLuOz4+UcHu36G2nUzrPVZN6Te6QWBeXB/UteZ5fJPvvKGyzHiR0lNaa/HWtoC2i/Ho+Xm95rr5fjSE1zUF+JFowxpKSkYPDgwSgvLw+9gwJdXtjVhHc5EsRrfz1GNFyVH5kb2RP54b/Whi7UhSkLwzcoQzMfPWNkH+V9WlxikBzs0DRrvzYhMxv8nFJe34Kc+V/g3v9ua3dd3ZG8MvESu+aUoY73PdYoXtZmL8j20tAiXr7piV3DY8XtEX0tLUmtPQePNQIIFFTsKK8T11JFKC2uFpMVJ4KAQ+u0kNQ2i4lhnDQh3PW/gN+lYGmcI8tJU2VD4GRaTg7jYwPPX56j0eQzkotRz3ydh9sWbMJ6w2JMXlk91hXaL1zWNLlQWtuCZ772L2rc9PoGLNpRGlAuZ/4XeGzhbt/3u97dggueXRVwHm1uL/78VZ7vu9twfz1ejvGPfoW/fuM/1pOL96KqsQ0t2mRP7rMq37+Y8JuPtuPxL/agpslaiCrR+lSzzv/2qhfX4I3vDvi+ywUJ+SyYIYUiO2vMt78vwkm/+zJou37B6r0NxQD8Amldiwu3vrUR+8qCJ92S336yE498tjtAUPsmtwyvrA69gASIxTsAqDZcJ9mHX14tFlddLheSk+3fWfIKOVlikn1B7wOtipkw6hfO1McJ+Rw2tqmNW3rMjlJc3RTQ7zyco6KhFYWVfiG5tKYFh2uaA94j+8obsFcnwDe1ubH7SJ1PKFNuk9Yo/X0wu1YV9a04qBMa5QKB3buNc468o/U4XO0XHmu0cdGle0YqG1oD5lTy8Ha6B/lTWa2/L5dUN6O8viVgXKhpdqGm2dhfpclvYJ1yN7PD+vqrSYd1e7mlACqOZ3kaljhd0LGiodWNyobWoMWZaJOcnAyXyxW6oAJdXtgFAMZYLGNsG4ByAEs45+uj3aZIsb1YrH4OzY6MD0lP4aw/L492E9pNXbN4oThx4ZQCaLqiMAAAuaVCW5OVnKB+IPgHymHZar6fZny/vxI587/A9CeW+ba9d3uPMbxQQr/6nBjvfEitahSDeXt9ocw4qk0O4jug7nCQgn1KgpoflLy0w7PV/PWbtMmlSp/O0yaB4dwzO5wEcpHP4MAs4ZcJtzZ56XNSQDmjkFpmIdTLCZzRZ0wKVnGGeprCmIxbUVghFiaMQo4Kbq+40SpWM299X+T7/P1+oZW3W7Co1xZ8YrVr1eb2wsuB51f4tb9GbZOZxnBvab3WRuu2HTHR9mw5VIM/Lsr1fTcuPJids9dCs6vn0YW7HS+QrdlXiWV7y/GC7tyNSO1/OJNvwH8vjX1WtlVfbyjfQa5YznQf5T3s+528B078MMNpg78t2r4OrUiaFYT7+hahDZfPhPnx7W68dZvkfgHPjoNOZBQ2Q+EXVq2P4b+W/m2q2kv/PTRfODS7PfK5dXTntMLxcep7mY1PEjPNuWp9ThZ+O4NI+hZ3jeX+EHDOPQAmM8ayAHzCGJvAOd8lf2eM3QHgDgAYNmxYlFoZHttLxCrs4CwSdiWltc0orhKThvsvHI2/KpowdiSqpst6SmvFOagEBpJUaCtrfdPVg5Ulx8eize01HXztyNdW9/uEERiNc44THlocsO2qKYPx7LWTHdfV3Xly8d527V9UKYSESAVN0SMFr+xUZwshHYVcqTdOIKyQfTRJUSAt0jTBqYmhhelkTeDOSIpsMC6prVVBTjp9Qs3Wf4v/sYH3y3i15MJI79TAZ1eaOho1glIDbNTGyAljhoPFNSukFjo2DPW3Jh8hNtbZICatWiR2Cw0y2I6ZRso4eTSbTJrtZ0TeF7s+Jd8lMiCQ2fzSr9lVvx51LaH7nazXbkLsNREQnOCxWHCR7XNSr7sdE/dwBGTT3yw0fLZt8DpvgxNUtJp22DXLqcbSDhWh3+x4dlYPElmn3YKQ2SKa6iWTlgFW1cebjHPyuXLi7uLXmjvfxwz/4ozzvuekDd2NrrHcrwjnvAbASgBzDNtf4ZxP45xP69u3b1TaFi77NV+NntzJnDLzKb9W9+zRXeN+hmNiuiJPmKg7iea3dI/Yx8lg6WRyraexVQzmqialevR+YfMmDkT+4xcfl4IuALyx5kDoQjbIW+1kgUOVjUXVAIBeKaEFOqcLOqqmxe1Bal0HZKotBMgonH3Tk0KWNZoAR4ojNeruC9LkcnBWMuBxAzWar36c/eKE28IsWZ5/tkEIlosMvVIC6/VpfCMQvVQuHoSzsOLTBtqMe3aml3LeqTJO+/1h/duMXd9MwxHku2dSRm6SQq/ZMyUFTubbJ7hMjWbtIecFKhoX6fNnt3Bh5Qusx2ton1Okht/YN6Vv4sCM0M+mRJ61k3m7iqAUdBw7U1gTE96QbQhHw6ehcv3N+p50R7IVLBUujVndZv6kwce32ehU2kVoM355LjE2nUOWCbBsUuweVgtN3KZDyOcqnIwMThYSuM2CTjiLM2Ya8J5Gl9fsMsb6AnBxzmsYY8kALgDwpyg3KyJwzlFnY05yPPKLdzb7Puc9PsdnOhZtVuZVhC5kQA58k4ZkKu+TGBfTIb6bZuw5ah6sRgVpbp37x9m2aRR6Oje8uq7ddRzQNLuJHZBmYstBIeyqWI6ECn5jpDpC/kJ2bNKE9QRFM+xdh0WfVjEJr2mKjC+QESnAqvhJS010XGwMsHWB8jGsfHPldqMmXAqTRo2jFEDC0QIEt8ncVFoFqaG1myRKTYvZdZVjrZyo21kCcAWtqddkCDYKpWaaXuNE2FRra6jbbLGy3iDYq/jm+TSqNhKCV+Hc22vKmJwQCzQG3wOf+bYT7b3WFCd9Sk7Yjc+G7WFspEDpxx2OljbOoaUCoPPhtCkjfwtIsaMgWKoIxGa3X5663WKUWYOtTIHN2mQ4IgD7FHJOhHv94o5y77Yo6PMjNj1f589Oe+ImmJ27yjU34jYb8HoY3UGzOxDACsbYDgAbIXx2F0W5TRGhpNo8quXxSnl9CxbvFBEefzdvLBLjOj7HnCq1Dv1JAL/myIlmtzM9Juz8dkJR1diGk/qnH9eC7o/f2IDv94tgPLefdULY9TiZXDr1sRzVX+Tk7KWgbXNqMljRQZpRPdIsV/W1LaNTRkJ4kzjVeMtIvCqTejkJ65uWCCz6tdiYPSLkfnu0RUBjPkm/z65RCJaCUOB2GaXUGMzHaQAb/bHD0WpIP1+XQtAmO/9zn0BlM0n2KJiYek20I8bH1FT7axSITcoYLQrMFjetAozZIY9tq7WV5uK2ZUJrsO1wW7gqSIHdke+rT+MUjomnk8m+9TnK/uykW/vNfsMfh1SEdX0JaUJrt5fv3thWbbaII/7baVHN+qiKIGd26SMRNFPWFHw8tbpDlTIbh6yqtnuHyCvqZNy0W9cIZX5tWh9pdqMP53wHgCnRbkdHsPuIWvqN4wHOeUCQo9vOCj3h60zCeWklhSGsO13lC8enKRIw1jFmt12NgvL6gNQiVtx7wWi8+m145swywJgKThfIZDoFlVVep3U7FYicBG6S9EqJ9/mxq9AR2nF9yhGVeVJprRAgh/YKHSRLBpVJlQrXrGFC2G2utt0vKyUezbWeII23tLgwaveKq6Uvb2A/kOeT0ycwrkA4pqDSv1ovgDtdKLCzQDDT0hjxqkzKpXbTph4ZSE3ffLMUJEF1K5QJ2sfkWgdFkFapx2Khw6weu/eZUauscmw9VloiKdQ7WYhqDeP95hc0He9qKog7Of26ujpkZWXhT8/9A7OvvhEfvLMA9//y56irq0NaWhpuv+5yJCQk4NvlSyzrUNKGmkg7jLGQz5vK4oGdD6m9RjhY0yqfQ9s+aS7thkRFQFOxorbC6tlVETSD67I+jt9U38HijPZcmNUbG8McW2fI0k4WorobPffMugEy12RXCR5jRTjBmZxy4sP+YEd5j8+xKRkdFm474vusejU+dpj3tjYMs8p9uvx8Tm5TOGkZ9Pu2uLwY2S8tZNlO6DodxiX//FZJ0D3w1Fzf53DON9WBdrxeIQiNHplvVWWl3OkL8t31znIK6/u36sp9UaUz0+q8dpjmW2GWT9EOeR37pIUe12Vez8mN34kNYy8zLWe8WlYaUGlpYYx2LSebxneNy8L0OByztkwtbZpezlbtU1apkczK2MlKXhvtpuxzdZrZsF3b5HXpn2G9oGe2v+/51/63uhR8iE1Mi43jiIp21Rd1D2AHmAAAIABJREFU2uYClWkLMU60lE7HhcoGcysoWY+TRVLZTqNFgt2cRC4YOxFw7Pwcrc7frA1bt24F5xzjT54EANi1YxtGjRqFtDTxrty7awfGTpgUojGwbIuxUEARQ3tMr5E0R7bzczWUDdgWskUGk2EH/qN6Qct4PFPfd999sa7crWDF4RS7c7IS6u3ed+FoVWVtpu49YWiXSbNLdCh5R4WgcmJf9Wi9ncnji3Lxmi4/4PZHLkKmQqAbpyzJLfOtUD17zaQA8+Wu8vB9V1DpeJ/0pDhHpsL1rc6FXRnJ1yntyc8mo672ZM3uKf+3BMdCXKP5F4/Bz845EUD7+ulhB8KUNOvtCKRWUJXtunyeKlSFkZLGaRoFY3TeSKCS0kOP1OwaTYzNkEGdzj38stgw7Rbgy9+E3M/jizIauN1qcieFt2SD5tvvs2vuX+kEMysTVa2gzwTXZLLIufDdcyIQ2/v+av7LyaEDOdlpOsyscIxB21SCB6q02c7MViJzeNotVMjrm6r5PassZLdnYVRPOGbu4USqlSUdRcQ17KvHSsNn9oxs2bIFcXFxGDNuAhrcwM7t2zB16lQAwIEDB1BXW4MxJ9sLu0qCpYIy1LTLOAg+pi/jKOiRiZCs4lcb6DKgsDjrW9iyqzxkNY6xeydZNtumHdLiydnijPhvdj/CaEJYC0TdDRJ2o0hhZQPiY1mXjMT85poDAYIuAEz64zdY//As9HcQTTEUzW0e3L5gk+/7VVOHRKzuSHJi31Tsr3AmWDr1iT1aqx7FVeIKM5jIrsPhm9BLQbkjUuV0Be5+d0uAoLv3/+Y48rvuSA4d6zg//6e+FCmUEhQj8140fgDe+r7Ip9ELhcw7rYpTLbaTSbkTYU6mQVOlxoFQv/NwLVLRjOzmg2JD7xOV9qu2sAKxcmuwEqh8PtGGV5BV/Xb4AuvYmP5aITWXdm6Kdmk95DHl2GTXF2SbMmz6rUogpxoVQVarxy5gmhSQbc2qbfzj5WKAbLNd5HJ57jKPu8rtaU9sBz1eBWH3D5/v9uX5BcQk3OXxIiEuJmjxSLpRpBoClrW4PPB4ORLjY4M0bVb7eLwcLS4PGGOYltMLj146PuT5mF26LVu2YNy4cUhMTEJdWzN279qJH159le83ACE1u20Kwo9879vdPjOBUU2QDi6lsohoujChYo6sUMishIqvq988185s29kcSp6fWSA4q5rshHc5noUVjdnBsezGYl8AsgjGuuhqkBlzFCmsaESOgxysnYXb48UfPs81/e20J5f5tAGRYOwjX/k+Fz4516ZkdEl3mIvTaVAPANh71Hnk6eV7yhzvA7QvOJpMlxXJRY+uQnl9CxbtKAUgzDKLnp7XZQRdwJl5qdOX+NBsMUlOV0xFtTrfWYTy9zY4M3t2qlF1IuzKHNgdgRMfx8zkeFwcq6XxOu3n7T52fpm5dl5q7Y0CgxTC+hmeZafXHgj0bZaYaSTNgqypaDelIGsXrV4ez25skvfHbgKsooWUY7zdYo+KH61c1LHrN7I9qQnWY5HKgoGsR06wVRYjVLTKKhRqVkiONFgRObI64QgcerZs2YKpU6ei1e1B0f59aGps9Gl2t27disysXhg8dFiINohG2AW4k7/YLUyaan99GkHrus0EaZWo0r53E9NvU7+Dttc+zI6gopF2WrUUzs0e6XAETf8v6p2vLaz0WqH36ai80F0B0uxGCbkCP7p/uqMALJ3BGX/y57nd/+RcxDDghIf8PrWjfvslip6e1/7jPO0/zuoHzrMNKhJttjk02VRZ9TfyqUMfX0CYgIdDOJNZSbnWX4dlhw7A093QB0nb/uhFHX48p0Ge5CKFyqPiNIVVZnI8itGsPOErdGhCv1aLXK2Kk3y1gN98WAUngnFembNFqD0OAo4dqmrCr2K1YDUzfxHwWzixEtKT4kzzB0tfXuNChpyMxgdFbzZPYeQUs+BLLnfwNpmCSWXCJxdlTI+nTbiN5tqBZQIFPjOOaX6nds+Zma+t5bEUHthBmdYCutenkbURrJWiMQe2WclcNELCrrwnxoUIj24Bz6hR3V/RgMZWNwZlJaNPmt9tptXl8T2XE4dkBeyzp7QOLo8Xw7JTkKXLK+3yeH3PpnGfmqY2HKpqQmpiHE7sGzoWhWh34Hev14v8/Hxcc801iGEMe3ZtBwCfsLtkyRJMmjZdqW4gVLoeFYJLqZjMmgnSKkORmcmyR2FxVprQ2h3DNM2XyfHCwmH3Difvst0hwhnn/em1nEc2d/pbT4E0u1GiQFtpl6lBugqcc5TVicnSuodmITaGgTEWJNyuzCtv13FW5JX7fBXvPGcEhvXuuoJTOC/7Qof+jwAwLSfb8T6j+qc73gdoXyTwI9p96+qB1Zyyo8S/oLG/k6wMqh36sToRjr/f78zPPE0hL2x7uGzyIEfl8x1aOjixViivU19gPOpQC1zmoO5WlxuTYwrFl4xAF45wNGpWmn+3RVoSud2ouZE+vvEGn1WnEzMzwdts4pqsCeP9bAJCyQjR9mmFxH87TVSzpoG2F1JlHmOFNEft9CFWEb59UabtgnNp564UiTpGXbNrViYcyyXZN433xq4qq3ttd3TL62izkzcMAca4UCCfjSNHjoAD2LNzB4YNH45evXph8eLF2LBhAy6/5kem+wRu09qi4OjqVGMZqxBtNxxhTn9AvRWDit+01CbaPWum/c2RAN7ehQM//tQ+kfE9b7FYlI50QCm7ZYfIpXrqupCwGyV2lghhY+zAjCi3JJB3dBFWBxhWmvf80R8l+advbgw7SnN9iws3v7nR9/2hi8eGVU9nIaN3qpp3AsDROuf+t+FEkjWNxqdAk4nJoSrSn7UrmfdGgsueXwMAeGD2SZ3mu+JUe+lEI9/Q6uweryusUi4bzrO/fK+zBbJHFu5yVP5PX+1VLutE6+30VJ10ndSafABAYa8zAcNENJz0P1b7WAnOBeXmqYrkgpZxMuXUIsRMsLUTnuzMfVX8aKVAZTfJlgK43URRCs12C3oqOX3LtDzGZhpuiS+CtIKQalfGd31s2iPvt8zhqmJyb2Z2Hs5CjD/yt3EBxXofy/QvYUw97Cb0UrvoKKiVoRGxsbG4+uqr8dprr2H+vb/A96uWISkpGbfffjuuvPJK/OxnP8MFF18asI/5YkN4PqwqhRyN2wGutw7a5MxlVylollk9LYZUdnYRm+2fdWedSY4/TmYIclwya4fd82qF77wctMLO/bA9ln7dBRJ2o4T0zzx5cGaUWxLISyv3AwDWzD8/6LfkhFj87drJvu9Wfr12cM5x8mPf+L4XPHFxGK3sXOTkyMmLsMmhoAEAK/Kc+UACwIYD6gKKnm3FNbY5Le0oKG/oshHEw6VZJ/zfdd7ITjuuU//3/eXqFgNOXqJONTXGqLMqFDhoOwDcNGO4o/IXjeuvXDZUpG095Q7cTLxerhT0R3J/3AcAgB39rwj6zRVG+h8rU+58C1NsGagny+B3KuURY/R9q5RHVpgJ32aTS6MvqRk+/1cbra1Mb2UnNMvJar/00H69doK1vNYqAqNd5Hp5m+2eVxVBX1qJ2Cnv5IKtNO+tskgTZFavnnBSU0lLDWOwLjtByloQsfOBNP/Nru/6Uhw5iRRtsu3tt9/G008/jb25u7E/fy+amoR5/sqVK/HSSy8p1aFintvms9SInMbS9vgOpGt9m1R2MxOSVVBZkFYRdh0v3Iaz0GKjibXqr3ZCuG9Rz4EEZ3e1enJ+XUnPP8MuSrU22RrQhYL8eLzcZ1psJQhdMWWw7/Nb3xehvN6ZZkrv+7v8/nNMI9p1NUq0a5LTR13A06eT6ag8xcZ6nZiiuD08bK1wRX2rowl9d+Cfy/cBAO6ZNapTj+s03c8RB36pVgKOGU5Xdt/+vshR+XBwGsvASdqhzQerAQAzY3Zj0uvDgU9/YVm2zIGVxhEHJs9utwcXxooIrUVZpwf93hChKLiAtYm61YRKCqlpiYHCrr68yrhmVr+Zv7SKZleaqdtN8BsVTJTtcvEa22NXj9xf70tqVY+dFYxHwSTVq7AYIHP62pmDWqWqskNeb33O4XA0uzLVk/Fa2FkwWGUbsBNcw3E7CkfgMktBk5iYiAceeAB/fuE1AMAzf38Br776KmbOnGl+XJOm+lJa2bRF3hO7IFZmkdlVroxZSi3judqZXzs+oIaC1bZt3Wa3XSXKsNPeEs7UR+5jKlRaVGjnT+8LYhYhU+qOmqN2Jbq+pNFDOVTVhJSE2C4VlGnhdhEg6frpQ23LrX3Ir/XVB/QJRc78L3yffzt3LEYoBoKINvs0oaG3ZtKmMi7IaK8pNtEzzThndF/lslK75lRD29TmRkOrO2Dy4oRml0c57VB3GUJf1Cwa2qvVdXq+ZhMLO+RwoXKcvy/b5/scqs826kwVVfr30HYGJ1M5xqfbjjiq8401B5TLpifF4abYb/BewhNiw7Z3gLUvmJZ1smhQVOnPgxzqFPl/REqSXO9weGOChdE6Xeql9s5FrAQAKTQYf3VbCJ9O+6tZPm+z3LMy2JndpFRqdO1yvcu9e6cFmx/La+jz61WJxqygaTYL4iWvp0pUZxUfYiuzcjOyU0KbXktUAlSZXYtWi4Ulu0mzlYBq1wKFIMBBWPV1u7ZJgduJplSWNVuAkMGpJk6eHPSbfa26VDQK+6lM4AMWP4IWO4Lvo5mAaGyL3XqCvqyxflMh2boqXRnr/eR/02BYPgHPrm6TbTZ9JZy803aaXb9liEF5YXdhwngh+DT2ptrlng8Ju1FiX3lD2GakHcV764sBAL++YLRtuYGZybh4wgDf91OfWGpbnnMeIOiemtMLt589QqlNXSEHsZwcnTRAPRiUNH10qj11omGTE8nRDoOcyUijp4YREMvl8cLj5ZgyrJfjfbsqMpjYqH5pYWu7w+2nB481hS6k4fZ4HWnUpYuEiq/5f9YeVK8YQJ2mdTxzZB+l8s3t8BHvCPYfqcT/xb8FAGjof6rYWLjKtKwxJ6cdymbpbY2IL1oJALi+7bemRaw0XvqJmKo1h5WGLK/MPE5AkYXwaRZwKqh9us/SN1o/wTIz+5Updcw0oH4hVRMu40JrSe3NoUWb7IRLOSaoaIhtzY8VhF1pwWBn5SR/yzCJxiyvj88UXMH31/fdQYAqvRbRKrCeiullUAmFQDzB251Pz+200f52GwUOu7ZZ97XcHdvRb8Ag9O3bz7fNzFXEND2Q5RFNyigEqNIXMd5/u8sYeF7GoGLBO5o961Zt0uPy7We9o9lCibEJ9spfWztmlU0+wonn0eYOQ0AO8zfrNli/m8IR4LsbJOxGifSkuC4XzXZDkfD/NOZcNOPFH031fa6ob8W5f1lhWm5fWX2A6XJ6Uhw+/FmwyV5XRkbptctxaKS5zYMTHJg9y1yL4xwELFuhBfxx4n8IwGd6bpejzwqfSWcPMnv5h6YBfXhu5wdKcyJcO0mtA4gX2Ig+qUpieL1mJaDaJ37/qQgeJU0TQ/Hfjc5y7EqmDMsKXUiHqsXBqZ5tAIAlnqnYf8lHQP+TARPtKucc9Q7MiZX9kivyAACvuy9GLcwXq6wE53BcCKx8va1yxEq/yl4p1mbMKshJcV+dqa/ZpF9NSJWCY/BvUuhXiX4sF5jshEK5wNHbxkRZRWsrrW/sysgxINtGYy3PyzalkkH4Nheu2iHs6u6Npfm7TX17Ss0tJOxaYOUPHc7bx94s1Hy7isBhtu/dDz6CJRt3Bx7fpDbT6xV0j6w1lkro2qeyGGemhTQuqpldSvns2gaZM7U1Fv/sFo3kL/rxIahv2FxKOwHcVJC2Lm459VExO3biE24bUMpi8Vilj5u1obMCckYTEnajREl1M3J6d50gP0e1ifR5J6mZ0TLG8L0uiFXRsSbkzP8Cn207jIZWN15YUYCc+V/gwudW+8qM7JeGnY/NjmzDO4HiajE5chJqvuhYY1AgDjsKK4QmxUkQLLnaP3v8gBAlA8k7Kia+J4QRZOqw5jfXXUzQVZDmsueN6ReiZOTZ7iB/s5ykq1qEZCTHB5gn2yF9Os8cpaaplfS1EQb0DNLarCqMysm00wBepv6TbY1AU2Agt5zDCwEA/3RfaVufmcmtHdLUNmQshu3vAwCWeqdaFrGK1O30mgBAokW+XCvTUpdPkDNEiHYYmMjMHNpsgq8idPmDNCmkA7IZR6XAaAzKFXAsG8FaIuNu2GptNU243btARUCX524XnMvjkUFrRBmVSNgqlkQyB3KMgrBr1zd7WZhX22mcrI7jNId4KCx7n023lO1WfmOHlmtNi9ll3VHxc9WXCXouTM/PkW45eJtCoDWz3VTkLX2R4JglwbSGoVG1rs3+F7uI6/6FkcgIlVYBpVRy6Zq1oQfpLiwhYTcKyBep3icr2ny0WZgwO8mFOSgrGZ/edUbAtnve34YJj36Nv3ydF7D9V+ePxNL7zml/Q6NAXEwM+tlE0zSjrK4V/R0EH5M+WVOHq2uyDmqTkN4OLQTkBH5EH+cC6z5NQ+Tk3LoyxVVCgJw81JkGMVJYadbMkBPJEYqLFJsPVitHEpaTENV+PjAzyZeDWwUZsO2CsWoRk5ftKQMA7Dqslo5Lmt1uMy4eNNcATw4C/nwC8Mp5gMcFuFowJ2YDAGAHP9G2XqmZG5qtJqRL7XtyKCuQrf8BAKz1jrcsYnVprQQAW4HR4iezIDZ2dVkJ/1aTSp/JsE5IMzOZMx7PTGiq0SIt2wmgBwzm16bpSLRtdlYVsjl2QrPsG3ZxGRI1k2u751wlYJY0abcrU6QthsnrY3YP9xk0/GY+1Ubk+ekXkqz6h7xHZphqKBGeACAFo3AC9JhhJXDbRdqWh1aNu6Kqo1YRPPzX38701xtUxli1qVlxGP7L+m2O/WPlBxXzZ4ddRaV/yLr1z5bdPbBaILJ1KQnHx9Z2OLfqTXYCt3ZfTS6Jiu9+d4eE3SggJwzRmmCbsabgGADg4gkDHe03eWgWVj1wrm2ZHY9dhPsuOincpkWdHSU1jvx15QQrzUFeXrlSbRU11Qyp2bWLBmrGzsNCIAjHjN6pwNXV+dtSYcJ8p6IPeaTZebgWw3urBXvaV26eusMMuaCm+g57YYUI0GWnOdJTWtviyKRVpilTzVUtJ8CqwrG0vsgw1r/6L/7PR7YA7/wQWPoYAGC5J1TwGH9wKlWLi7K6FvRKibfPQd3aALga0Rpnv9hkpSWz0tbbadWs0hjll5mbN1uZPVsJu1b+xYeOiUUOvSbCLLq1cbJldi5SMJOaWTOTSOlqkm0TTNAf2Tjwux5/hGSbgFlaBXZjts+v086PViGlkkrqISmUpmt+vWYTWKPQLfu13TnI9umVSVZ9zc73z8rEORz/WystlV1d4URpttvHccYaBS0uEHwNzfZTSakjsYlPZbrFyxXqtjMZtmuMzTWz3y+0AG7WJ9VuUfD52gl/VqbIdv3ByhLBrr/atcHyJ7s2uBxaIvQwSNiNAscaQye172zWFh5DelKc/STNguG9U1H09Dy8cIPfJO/SSYOw6w+zUfT0PNOgGt0FzkXeTNVJOuBPgTKmv7qALH397PIxGlmomd867UexMTGIi2FKZkNGdpbUAnCmkezKfLZNRCB3agoeKdIS45R9QmVUzYGZobWMss6fnp4TdtusaE+aggGZahYBcvX8ppnDlcpLYeqJK0/2b6zcB6x9XnyefKP4X7gCWC/yXb6adY99pcf2I6G6AAAwcYjawmTe0Xrb/K0AgG//CgDIHXi1bTEr/986K4HTZmJeXGWeEik10Xy8T3IYRd7q2PFxYpDJ0I0Xsh/rNatGodtMyJDvJqktNTWHNsy4TU15jT6RJmVU8k57HAiyKhpZWcbs+ZL+rvGaQGymifT7PQceW4/xvJyYUOvL6FPr6bHrg8cscvqGM5r4zULV9wknXVIkMXMBMBtKgwOVBReS561y+gELAoaqzPqIiqbdLkCVkoZWv03hZkbqzqmmTLLzFLF6/dn1Lysh3e5Vapdey0q7bNcGOU5FypS6u6E+gycihhwUhmV3De2Y9D0aP0g9OJIZ8yYOxLyJ8yLRpC6DfKkPyFCPnC2jazp5tx7UzGlVfSABMYEsdZDXU3KgsgGjHAjieg5p7QxnUaSrUVrbDLeXY8yA9KilACusbMQ5o/tiVX5FyLK7jwiT3pAmsvBr/0YpRuoePyhDORK4jMTsJK3WCX1ScaCyUTlAx6/e2wpAPWDWwu1i4cfnF8i9wPPTxOdTbwfmPQPMfgL4kxCeq3gampL6A5BmzxwoWCJ8e1OygfK9wIun4UwA8Vjg06aHkvPjY2NQVtdiHdjI6wW+exYA8FHilQCa5NGDsHrGrCY0xxQiJQc1x+J8rIQ9qwmYlTm0rCdZ5zNs5sdr1MibKaKNE2yzCbexGWaTeWMZs3qM2+wmybbpiXxBoyyL+FIXyb7eaBJ8Jjs1Xisr+oTZeRmbaHYLraIx2wm78nrpBXarXL52mtAKi/4ZjtbLOvWR5S5+s2NTn0XnWmcrLbblNTDZbKqNVAkipjC38MmQAduMz5DZjgoLPSaXX8UoSDXvtpG2MCM9Bz8TqgJpaHPg4GPZXTeLfWyOY3eucTEMZpdNJXr48QoJu1GgVAs80lUWWJbkCv+4yyYNjnJLuh5SwJgwOEM5Gm5Fgyg3sp8QNFSGGDkQ2aWfMHLoWCNOGe5PAaQ6lu06XIdLJ6n7ZuvhCI7Q2l3581fCr/yX54+KyvHlPVcN+y/TviQpmDHvOiw08AEaIM6B6iKRU3bnh0BdqRiEuBdvuFNQmHEa4ooTMddzCNh8GBh5IZAZPCZIX8WH545Vjj4sfSl9TVHaCximaOL93gYR7VkuBEwreln8kJghBF0ASM4CfleOzas+x81LvKjT+/c2VQGeNuHbO/1OoOGo76en419FUcYzSu0orW3GKcN74ZCFJhV7F4n/Q05FU3wvAE2WFhb6ibB+UmRlMmyZx9Q2v6i50GBVV0mVLo8w97/DKi20dmYRi33BnwIiqwbupxJcyVzg07Zp/8wmt8brYSZY5x0NjBxsLjRba219zdD+x9j4EMvrITUuZoFugoR4hetjtmBh7DtGbbAZZppdywUXGx9gq8WxGAsh1E5wtopRZudjG46ZaZuNds1KW28pOJttNDl3YzkVWTdcn0tTATHMPVX2M2unygKofDZsowYrLO6YPetmKZOszoVzbp3H2bplNvVZF7L32TWv1/YeWK3BHCdCcJcXdhljQwEsADAAgBfAK5zzv0e3VZGhq6Qe2qal1jk/CtFouzor80R6n8lDs1BaKybAoXJbSs1XYlyMsn9E3tF6jHHgFwwIM8DM5HjHiyaMAa1h5lUrKG9wlFKpKw+kn2wVJsxzT46cCbPy6eZ9CU/RGvTGOIwdmIP1B6pC7uLxcl3gHbuCLtRv+RDz49Zh7o46nIP96LuzFdhSGVguLhnoNwb86E408UQMrduMIUwr87nOxPcX64F+Y3xfn/5yLwBgXeExJX9x/aRbNR/xiX1Tsb+iEWkJcWDwYnjZUuDNT4GBE4FxVwD9xwP7vgbW/Qtoa8CNqXPwn8Zpmiab45SiV0VFd64ynHMiSnrPRB224ezRfbFaatRn3gV8o+W73fBywC5Xx36Lmm0/xEt4POR5Vja0+SZPQfeoZBPwwU3i87xnkfdBLfqkJaKq0VzjZSXU6oMK6Y9hFSnZzrRNmtAa21pYKRcxAn+wsoCw8uH0+RYiWFDSB62yMq812ybHX3PNrrXmSn40LtCY5Q7uZXg3m7kamPmyGjEGlqprNqlHxazaRqstP/muj7ahxsTc3VhPueY/LccVs0Bjh2Tgq4BozBbCYRiRla3GMlvhweJHO/NzO592xswPGI4+wlKwsdnY3tQv5os64r+t2aqqI7FKkTBf9UECm+nNlQtCztpkvOdmZcxSJqn0yeB2W7etxcISwW5sluO5efRkZ88SYG36HGXr/k6jywu7ANwA7uecb2GMpQPYzBhbwjnPjXbDwkUOTHY58zqTL3aUAlD3p+tMoq393npILAQ4SbUjtQJDs9W0UoCYTDl54TW0ulHd5FLKiaynsdUNzoEJgzMd7SepbXY5DojVFdmk5ZSeMiwrIj4sjqporgbeuw5xADYnARuKLsX0+DK4Gx4CMNxyt4r6VkwdlhV8sLYmYPEDQO0hYX7bWI6rATG6lwGVyERLbC+kT74CcLUAI2cBo+cACaJ/ltW24LynlmFEn1ScMbIP1uzYi+Vn7wdWPinqf/E0IKU3MGwmEBuPG4prcXl8NZJTbsDh6lak83xg7T6g3zigVw5wYBWQOVQcB+Ile17MVtyStAInrWVYmXAIA19qAVJ6AQnpQFUhcPnzWqTkRqDPaByqqEUSPEh9Zy4OJG0AdmrnevA7YN2LQdfmceTi8SQATwNF8pEYOgPIDg48Jk0gpw3v5Rd2J1zlF3YBYN6zwKm34oE3vsRfDl2HrOZDWJ5wP4BLxO8tdUBjBVC6TQScik2AK20IAI6pQ7NwdNvXSG3tBXgmAbHxQGs98Jq4Hph+BzBwItIS16LN47UUdncfqTXdbjVpcbmtNL7Wk/wYZj7ZsTIfV4kErZ9wmeVWlUG/9IHWjIK6PhCWrC5I4DMYLQDBgmJTa7BwaYy/YCdY++oxueZ+s1zrhz+vTFgGSUHRbEHCeCyzMsHnHlpjbSZ4WvlGpyaIa2ImjEu/a/1CR1FlU1A5wNpSANBZsKiuCIYxCQ9HQLatz05T7HQfM4HUpJjLcN/M6gsaA8xkVl9QJ2vC1eyGvZ9CIftUS87OxuhiYKaoMNvWZtGP7X1s7cdZ8/qsK/RZfJj8ZmmJYLPYZDW/DCdwW3ekywu7nPNSAKXa53rG2B4AgwF0e2HXSU7VjqS22RU6L+RxSn5ZvWPhTq4QDsxSv6Zl9S04a5RajmMAqGkS2p0RDrSsALCn9P+zd91xVlTX/zuvbWWpuOWVAAAgAElEQVTZZWFpSxMQkN5ExC6KYkFsCTGiRjEajRqjsUQFJf7EqFETExMTY8MSE0tQo8aKlYjSi/S6y/Ze3tv3Zub3x8yZN3PnzLz7NsBC5Hw+uuzsnTt37ty5c8r3fI+Z99kORwspMunkah6ocsfrawAA82eM2P8X/9Px1j93611xZM0bQBDAZzOBb7oA31sI9J/iOq2qKYZh2kbc+PnPcWMmgHkwYLoxW3menCKg72QsifXDvJ1j8Mrcy3Hy/R9j5hG9Me8MvswNRbauOXEQVuyqQ53SGTjhFuO/r58CPrjbMF5rtwNqGya3bDTGu2J5spN3mY4HnQKUr0VOYymeisDA5dQAe5QuiBcORUanQmDDv4y2/7jMceomenVKzN97nonBo44GOvUEtn0CdC4GQhnAgOOBhlLEX/gBwoqgAM56kb1fUhRz7Ay0eb2AX5YB/9cb0JP9rKjLxjldXsPrNTPRN1AJ/F8Xtk8AyIBpaH9mHmgCMP8aoM8kYNd/jGP9pgDTDYboTRWNGFmcj62VPBTcqy6pl3LiRRrkRQwEmFEDSdIWwCdn1yPSl8+kPBC5nZ24UIycctD+bwVoMaeQrit1lqpqYoxd8RZk6v5yxmVUyK3loqKE3qJPvYxhzU2lDIR7Z01Lyjads8IORwKNi94F3wih7Zhn3WYfxdkL6u7lvPGDJHuWf2mv3u4VKfaN1nk4nTzeERYSzcytK2LI9CVCf7k2UnmuzInifbU3Z1hWxK78avH6acxtjLPPdcTHSrc7vb2ukwrVl6749qYo9h/CnxTWUE438m0cP2TsHnCiKEp/AGMB/KdjR/LfCX2j/1voyt4Qqu96yhFyJT6+S9LapkLTgUkDvBVcTlab+ZLpGJS6bkA3ZaWk1nhueWmyIlMdxHRKKZGUmrnmg4vSr897IElC1SzFub0R7nZLzVagbgcA4P3z1+OKhcsxf8ZwbHzzYcwPPw201gBPTzfaBkJAZmdACQLRenytxgC7Lj/2YiCcbXzhsroAU64DwgaR2kdvr8e3O7YiJzN1qgQZBGzd0QmXGf/Z5Jj7P8Rh9UvwbL93UVFbj+uiP8ZLPxoHLF4AJNqM6CtgED4BWJE5EbXNbfigyyyMOuZM/OKVVfj8wpPQO98kfatYD2xdbNxrOBNq9TYsfu+f6BeoQL9+AzFi44/wi+FjMXjyAKP9iHOdY+w5CoNjz0GBhrV3Ho9f3PtrnDJpNGZk8+8tvZ+u+tThLGD2Pw3jvs+R1uHKFuDFSa9j1n/OSbbN7Q4c/VMgvy+Q3w/QEqj7+DHkb37NavKfnJMwSVmbNHRzioBL3rT+HgwoltOKEy/vvVcEQSb31y5++WdepYq8DPPdtbyhLWPccedzCq/IFVDLzF1Xgc1ehpGYhUMLxziDWCRp4soyyeTaitFv7r7Ey3OPTeR7kGNjNn7S2uHukyXw8lg3u2r4iK9fBMsrZ9Mv4uSlO/lF1/wisV6GtVeED0g/WME1t66695HGSfbdNMv1hIIB2NmPuGfH7U3i2mFJ3bhBuMjg3E2svn3miX8cwnqXHJPX/Hpsi77ntLu/dnhufPN8va3d74QcNMauoii5AF4BcIOu6w3C364EcCUA9O3btwNGl54kCR86eCAAlppwTjvR0SEx5OsdxtwM7+3BUt2wB9jxuRFh0hJA95FATlcoZmTIi7FSFFKSZAqgk9DHprggS5rgCAA2mBDCXmlEnUnKGw1jd8BBXmP3/neMnNOrTxi4fy/c1gz8dqzx77N/h4T55e5bmIM71VNx+Jk34OI+1cDyhUAgaECScwqBQAhadjf8/osyjO+qYmefs3Hr0hxsn+HNfP66mY8sIy9/vQuAPIfA7tpW7MZo4Me34w+L1mL98hKg7yTgYtPQq9sJfPM00HMMMPhU/O2NjXjxq1344ynj+bI5RcOM/0zZU9uCH/1rCG6YOhiXHT0A0Xv+7TseUryPO7w7EM7Gm9pkjMwf6tn+H9/sBpBkwXXIgGOBK963fq1siuHI/l1Qn12A/tEXsP6e0zzZsBePuBc/W3Menpk1BA9+tAvdCjpj0iUTvMetA6OKO1tkYqKsKqljj2+pbGaPe+V/pXscALZ6XMOLIdpL8ZeJmgLuiGN7WZRFcqf2El3VCFFIdjwS5DdibivXj/jecVDEdXucEesahghKfAIyZZcslJlpGMmQWgHea6c9hmv74MXpn+PFU+FH7tSeeEQaKGZrTOkapKKBIlMqi5N08kHtws2LiO7wJZCzSdS13uX2DHfnxg9HlFxinqLWM/A+zzq/nQRoXjqaFyIG8IYk67ru+Yz8xueZ/uJDwva/JAeFsasoShiGofu8ruuvin/Xdf0JAE8AwIQJEw74J5cs2t3xkd23VxukS4eMXbd8uskg6zlOgBcrVRuAp6Y54aM2eQTAI5kAVv0ZQOr6nGQA9E8Dkkx5b3mZ4bSMXVJi+3TJRn0LX6/TS6rMkkqdDuK6yQDwppmjfuMph+/fC+9eavwsGg6Mm41NH2wCYEMAKAGgeILxnyDN0Tge+uTfuH3cUDTFVACbfC/VIy/TKoGVSr7cUm0MK40az76S3xc4+S7rV1ovkwcW4t01ZV5nWUIs2YtWlOKyowekbE9luw6TdML0L8zG9uoWPpJtk7iqoa4lzkJhOWmKJaAhgJ49eyKulPu21TQdNc1tvg6xwpwMtj6uVxpBqQeMuZUpZQPwxEwkkWCAjWp5RcC8lLamKBftbJ8BKhqOnBHmishKGGoczLtayKNuZtaAqDjz+bjO37nor6igcnPcLddpEHMGkNgP99zFe6d7IGcFN+/cOvAyrtrjWPGLuHoL/ze/M7zZk32u4vs3/o9e5F1sTqlpNdqNx/+742aU7N6Jf7z6OgBDT3ztpYX46+8fxp6SXcjOzsbXG3c7rykRseQjrRLnMXfCid1Z/+WXX+LhRx7B4k8+Q011JbIyMzFkyBAce+JUnPG9S9Ctu0EK+fTTT+Oyy5LIodzcXAwYcBjOuPAiXPDDH1nHFQX48J238MKTf8D2LZvQ2NiIgsKuGDp8FK695mqce/aZtii5N7M1O27GcvdDw3gJl8ZAElAUX0P0tZcW4tk/PoJdO3ciOzsbdXW8oxPwh9b7IRGMckVya2Bfy8MPP4ynnnoKK1asQMCP4W8vygFv7CqGRfgkgPW6rv+mo8ezN4Tq2qYTydtXsqvWUBTTIVP6rggRTdlZkh8I/RGRP36SbDTlemDEeUDpCiARA6o2AEv/Yvzt1TlYDqB+TRFQeA1wzA3sdcrKSnFSYBkGVFYgtK0XxiibkRXtBpQ3A9WbgcqNRuQ4lGFEwLoPx1azlEu/wmxUNMqVRAKSpCoZoSCA9Ixdumbfg3it7Khuxp76KI4eWCgded9rsm6R8fPMhwEkYcPd81IbmWVm2StZL+zG8ibpHPAzR/XEnz/dZpXKSiW5GSGcMEQ+v/yJT7YCSOZqphKaj6uOl4u8/850Gry+vAQ3TxuSsn3vgiwph02LaSyMLJaDum+pMN6PbrmpURONpvHkF9HxZFdOkzzFy6j1IzPxUoC8rm04YNyyo9oNayV0iV1ERZCFBLugvKnhtZyRJRJ/cfPsyOcGz8bsNhxdTVx10DlWVpmavm6WaQkjniE+c8GYCWVm6iLcOd8yJGOlHmX4vCJwXnVxjb/xa8ev7I8Xu227CKU8z/DPQfYyLLyGLQNH3rJlC/7+/NN49rUkCULpnhLMv/UGTJ95AZ5/7hlkZma65pnt2xXF5wYleUzmNPPgnx57FPfO/SVOOOFEXHvzL1Hctx/yQiqWf/0Vnnjiz/hq6VL84bl/OM598I9PY/Bh/ZGpx/DS317GgjtvQU1VFR57aAEA4K9/+gPm3nYzzv/Bxbjz9luRkZmFz5evxacf/BuffPyRaeyaQSRbv2IggL21NGy99jhTUvVXUbYH82+9ARd8bxaeefppZGb6fz/8ovjtGkMHhAevuuoq3H///XjmmWcczo59KQe8sQtgCoCLAaxWFGWFeex2Xdf/1YFj+q+EisZ7weH2p6wtbUib5Oi7It+WNaBTRsjIYWmtw/e+/j66hsyI2oXPAsPOTnoRe462zhu6ZCrOL9qDX3V5B22bPkDneAXw/lzjv6FnAsPOMgzj9+cCrbWYCGBiBMA3xn+vZwD4xPzPQ26O9MJbmOdQ2nUA+Oxh4MN7DVbca5e6EllUTUsZ0UolMsbZgSqPf7wFAHCThFG01+XrJ42fZuSWoImG48FfCII0uCgXa0p5RIFdcjNDbDSKk62VzcjLDEkhTZpiCTTFEnhz1R489gOp7lnxU0qzTWZYWYP6iF55eHV5CR68YHTqxgA+31wtlYdPyIfi/Cxrz/bzgpPvJDcz9WeVcu7pXeKmww6ltf/dy+HhR+jDiZ/jxCsS51VX2Suq3NmHoMrvelw0XYTAc2Msa3AaYXuYcRV1ynDUTOemTVQouef+bZnzPbTPP7UXHQoy5ZI4G0q8Vxl4OOdMF88jxztZCNy1u3aKYIMAVPCKfDW38XtOjU9uensQbl6RMq88d8D73fWDyLYPHuxh8DNjE1s+8sgjGDJsBIaPHmsd27p5M1RVxVnnz8IxxxwDAJ6pD22xGADDOe92MKSO4gJc7q27jZfh/tUXn+Leub/E9ddfj/sfeMh6Rw7rmoPzzjkbV1x7I5574SXXuUOOGImxI4ehd34WjjnhJKxevwHPP/lHy9j902OP4sRpZ2DBI3/AwG65iKsaegybgPN+cAn6dclyjMm+mkIBxXE/7bTtLRGZsr36icViDoeZ19acUHXs3LYVqqpi1kU/tJ6v37j8vkG6bqyBSIZbR/OKLPtBqfeVZGVlYfbs2XjwwQf3m7F7AGSN+ouu65/puq7ouj5K1/Ux5n8HraELJPNacjM61tdAXnrZyMV3SXRdR3lDLEnk9ML30LXZMHTbLn4TOGKGJ/VdbkYYpZ1GAhe9jKOCL+GBI14F+h5t/PHbN4HXfgy8cZ1RggbAll5n4tb4FSg79xU0nvs85sd/iC+H3QGc/Rgw8wlgzofALTuAKz8GTroDAFDQVoqVmVcCb/0cWY07MV7ZgCNemgy8Pw/Q4kD1JmDRta6xrSlpMMrXtENW7DKgNQdKyax0Rdd1vLR0F7rmZmBc3w6A7UdyDfbggDF/ZNTJENUR06qs57ayMYbhvZK55n7G5QffVli1oVNJRYM8ioDEAY+WGP6jZqRWNoeYlNWhPT1y621C8yBT19jKpQ/I1cveWdOCTpkh2/NMDXkb3D3XU9mv8IChb6pwR9oAb6XdyzCp9oj4qpruqZzZc3btTTI9HDZibVyAV7pkIPeiQV3GRBdbLaeEISJpE8DUtWVJmfx/B9xs2dy7IToWOeNJPMSPR4wic8a3c13IOD/E916WUMwLlbaTieQD/iWJvMVv/Pzf/PZHLySDn0HrycrrMbcNDQ3o2ikL/3j+aQDAC889g0AggKamJgQUBXO+PwPXzL7A3pH1z1gshoULF+L0c863jl166aWYcfqpAIArvz8DiqLg0ksvRTCg4PHfLED3zllYs2YNZpw5HUcNKcbNVycNh88+eh8XzzgVhxcXonPnzjh35kxs35JMf5k3bx66dcrEts0bcdVF5yEnJwd9+/bFP158FgDwyt9ewNChQ9ElPw+XX3gWdm3fZp3bluBzjZ/6w6PoUliI+++/H9wzysnJxowLf+CLaNE0YMTosWhuakRFRQUAoK6uFl27FXHTZsFgLePe1rUOYOe2rbj2yh9hwIAB6F6Qh+lTxuBXt92I2tpax3W3bdmEmTNnoqioCP2K8jFt0gjcdNWlSCT8v43z5s2DoihYv3YNrrroPBw1pBizL5oFAFi5ciXOPvtsTBnRD0cO6okLpk/Fp59+ap177Y+vwOUXGuXsZpxxmvV8AWMv27BuNa699PsoKChAVlYWpkyZ4jj/oQW/gqIoWLNmDaZNm4aBvbs61gBdv6CgAOMP64FLZk7D0iWfO8a/4N75GN2nADu2bcH3z5uB3Nxc9OvXD/fccw80wRBeuXIlZs6cicLCQmRlZWHIkCG47777XG3omtyYSb7//e9j3bp1+OKLL3znd2/JAW/s/i+KBR3qYDbm99cb7tpBadSQ/a4Ief4Hd+8ElK8Ddi0BAAyJPg2NDFdGNE1HVVPMQQDVEOkO/OhtYF49cPNW4MefAj/5D/DzDcC8ejzb/Ta8pJ6E7MOPR3zgqXhSnY6NfS4Axl0MjP4e0Hs8kJUP9BoLHHczMK8ey5UjjM6X/gWT35qKVzLuRqS51Dh2nhlBXL4QWP68Y3z1rXE2j01GmmMJhAJKWp74DkDIeMo7Zr7oD47ss8+u4el1rdkGtDUB3ZIR5bWl9UlG4hRS0WAYA/27poaQk8JP0eB0Ayd+hnGdaQA+JBlF1XUdFY0xRyqArHCGCif3vW0QjrnYlRmh6IoMR0FJHaV4yD2jsoaYBX1ONedkqPlF9b3IoLycpCJkloSDEgNOGKZ93dphzy6CF4+l4ZWnyObMCsdkYLtAknmemnOfzwxhzpz1f3XzmHAtGdiwrwGq2/7v3w93LVcUmWkjwroddYjN9sRWTWfLkPqIJG08NNx9nlfk32tt+ufs8n9rTy5te/IPdY9/c7+nOr58+XLouo6hw0cBAFatXIHBgwcjNzcXgI5v16zCsBHJvdNOzrRkyRLU1dVh3JGTrWN33nkn/u/XRvbe7b96AF9++SXuvPNOxzVnzJiBKccei0f/+gJ+OOcnAIB33nkHP/rBecjOycHv//wsHn/8caxduxaXnns6yveUOs6/6apLcezJp+L111/H+PHjcceN1+K3C+7Bwqf+ggULFuCPT/wFO7Zsxq0/nWOdQ04F+zvY2BrDN0s+x7EnnIRIJOJbL9ffe6ijZNdOBINB5OYYuunosePxxj9ewp8fewQbN24E9wSCQYZ9Wgcqy/egZ6/eeOSRR/D319/Ej6//Bf7z+Sc4+0zDyCSo85wfXoCSkhI8/vjjeOGVRbj+1rkIRyIOg89vdf3oogsx4aij8ehfX8A1P70ey5Ytw9FHH43q6hrMvf9RPPTEM8jv0gVTp07FN998AwC48Re34ZZ77gcAPPjwo47nu2HNSsw+5zTU19Xhz3/+M1555RUUFhZi+mnTsG7VCse1Z8yYgeOPPx5PLnzZWgPLlxvXr6mpwZ/+9AQeeuIZdM7vgksuONu6vl1+dsUPcezxJ+D111/HOeecg7lz5+KZZ56x/v7VV19h8uTJ2LJlCx5++GG89dZbuPHGG7F7dzJ/nO65pqbGMWb7PZOMGTMGeXl5eOedd3xmde/JwQBj/p8TkRSio2S7qQSdPrJHh47jQJSVZhRzfL8CYLWRg/vesPmILfdXqKOJFEzMOYXGfzYJmp7J3EjIMiZSyQ3Z/4fMUBDvjv8PKjZ9jc+3N2LMKRdhwLE/MDTtzHzg+fOAf/4EGHsRgKRnu705t43RhHTe5YEojy82IMyXTUlNerTXpcIsC24zdnMiIWyP8qy3opDh2iUnI2WUcbeZhz9Q0okVCQas0mOptiSCRos5jV5SbcIkxaiTn/TOz/KsGesnkWDAev+8hOCxp49IvedRqS3ZCHN5fVQ6JYTG6Yfu8YSdeTirWtt4g9NrL/Lqxy+Xt81jfu35lXaDY32ZG3Lvzj919+c2Ut2wVS5YKFNWyNVGIpIqVx+XgYmKzgJmzOK7wa19WoskbP1gCZIvUcQ1IBNV9uvby6h1GOfC37zWmxck2a9UkBc03zeX12eavPYTr2jwsmXLEAqFMHio4YxetWI5xo0bBwDYsX07GurrMHRk0ti1R8iXLFkCRVFw+LBkPfSBAwei/6DBAIDDDh+Ko446CoCznvR1112Hi6+4ylG+64477kCffgPw+2f/jq6dstC3MAcTJk7C8COG4tknfo9Jv33EanvpVddhxgWzMKJ3Z0yYMAFvvPEG/vH80/hi+Toc0b8HmmMJrN68A7+eeyt27tiB4UMGsUZrU30tYrEoehcbzmT7GkkkEkgkFDS3tiGRSCASduoRmqYikUigtrYWzz7/Ij54+w0cP/U0ZGcbuspdCx7GdVdcjPvvuRP333MnCgsLMXHK8Zhx4UUYcN5ZRiceJF3jj5qC6aeejO55mahuiqHXkNHo038ALjtvOpYvX4684sGoranG9q1b8OjDv8HZZ5+NnTUtGDT6SEyfeQEikeT+77dWLp1zNc6/5ErjuXXLxdnTp6Fv37549733sbXGeH+nTTsNZ50wCfPnz8frr7+OfgMOw2GDDKLMIUOHWc8XAB741Z3o2bsYf/3bIowZ0M08fxqOGD4cTzz6AB55MhnIuO6663D99ddjZ3UL6lqNb+7tt96Cvn374sMPP0QwFMLa0gYcffzJOP+Uo63r2+9p9pXX4pqr5qBLTgRTp07Fhx9+iBdffNGCGd90000oLCzEkiVLrOdy0kknOebg5ptvtq5J8zZt2jSMGDHCcU3AiMiPGjUKS5Ys8Z7UvSiHjN0OkAOlzu6bqwwP3yFyKrcs3lgJwKyxu34NAGBT9+kANvieR8yefQrk53T5rloUZIet8g8ysqO6BRdOKAaOuxlriyrws01L8fqAKUlrZfBUg/W3Yq1RIimvp0U+MrydtWXXltZjyqCu7Tq3o6W+NY5Vu+tx9MBCFEgaL/tEiida/9xa1YRRxXKQcoJ55ks4Gyi/tF+hnOHVpmq+TJF2+fOnBpStmyRzM0WZZ4zpJdUeMJT9EV7lvhgZ2qMTvi1rlHp/uJItXkKKrqxzKBRUpNuSstq7wDtq3OLBouzlEPPyr3nlZHnBm/1qlXqVPRJL45DkZYZdUcnNQq1eWQPLi1zJLjLlT1wwZqafbVXNKduIdpVMqR+uTUFO2GG8cs6JvMyQY+1y6BrxVks8ah+nOz7umEjyReJVN1qWPwBv3wqUrQYAdFU15JkGb0YoYFvgOg6L2RAUkaQa2zWhorP5YDLDASCQ+pwMTcNhJgQ2HFSA4jHA6UauqFdAwsvYXrZsGQ4fMgwZmZnQNA1r16zGBecZdcFXrTSicfbIrr2X0tJS5OXlIRxxfp9oX3OuiuSZM2fOdEQzm5ubsWzZMlx9w00IhZL32adfP4yZMAnf/McJY51y4lTr3wUFBejStRuGDh+JTnl51pUGDDQM7pLduzF8yCD23r2+IVUV5Rjdx1kKbsWOKsfvM05I1jUPBAKYPvMC/GJuEh57+OGH42/vfIJvVy7F2q8+wxdffIkP330L7yx6FSWb5mH+vLls1FXTdcTb2vDog7/Fqy+/gB07diAaTTqONmzYgInFg5Ff0AV9+g3ArbfeivLychw+ehIKernLmPrlhJ86/Szr362trVi8eDFuv/12BAKKBYXWQ2FMnToVzz9vGKpelTRaW1vx9Zef4/Jrb4QSCDig1CeceDJeevEFR/uZM2ca/ZuzEG1txaeffGJeP4C2toTVx5RjT8Sbr71snUt75LEnn+roc8SIEVi+fDkAoKWlBZ9//jluvvlmy9Dlxpy8Z+eY7fdsl27dupmR+n0vh4zdDhDaKDvY1rUUaBmCnO+aEPtmcUFWsmSMhJBi51UehJN0S/mQ0tfqw3AJABh5PvDBWmDPSiCvp1V2qL05t5ouXzv4QJM/fLwZQAdFdQFgjatiGlQNqPUhbrELKboyBt0uM783JyP1c6ZcOtmyV9+Ydbl7dJar00xRqqMHFqZoaUijWapmTUlqEi6Sb8sacWT/1IRTQNKIKfYxMkmIjMkLTizK7tpWHHWY3H3SO+gFvfaLynkZMV6RWq9olxeplEjyZJdIKMBG4rowRFQAH1ET9x/uXjmj2l3XljOSxd/dbcqFXN9tzLXCwYBDseXm3B1FdjVx3RtHmCX6IrjoqOu+ZAiqgs69QorlmcsFZtiYu+RELGSYXcQItNd1/MZk/e1ASILxgkt7HF+2bBlGjDEiudu3bEJLc7MV2V2xYjk65xegdx+3EQUA0WgUGQyxUKph9ezZE41tySO1tbXQdR3ditzolcJuRSgt2eU4ltfZ6XANhyPoZD+m65YBHo1F6ZBLCgoKkZGRiZLdu8w2RqP8LoVY/NmXyM4I4eHf/QEvPPuU69yH/7wQhw/sh4G9uqGgqBfKmgUWZR0IBoM4cvIxmHX2aWiOJfDl6k34ycXnY8G9v8KN118H6M65ozX36IJ78NLTT+Cuu+7C8LET0KpHUL6nFDdeebFh+OqG8+iZl/+Jpx57ALfddhuqq6vRu28/XPrjn+KOm5NVNPw+v1279wCBa2praqCqKubPn4/58+ez7TVN8wx4VVdXQ1VVPPHoA3ji0Qc8zyfp2bMngCRLeX1drdT1A4GAFR/pnO9M7cnIyLAcA7W1tdA0DcXFxWxfAFAjec/2UkNZWVlobU0fxdUeOWTsdoBsNglGOrLOLhEwnWrCFw+JUz7eUIHe+VnGMwplACPOT30SYEGJZI0BAFi+sxbDeshHsojx0k5AxMqgqcAHdxulkIacZhniA9rBvk0Rup5p3NeBIrqu47kvdyAzHMDJQ4tSn7AvpMmkM+2W9HC3JVSM6SMf2ZWtg0sGdNfc1O3LrdrJcp+C88cX45kvd0jnGq/ebUSA+ktGmQmuPVnSaKQo5FfbUxNOAcDLSw1FrE3V7cEdVrIjQSiKnIOHIldiDqSXbCxv8s1/JyONg3R7sT2v9WDp3lLJMyh7XrsdXEJe8FUZKC0XJePg3WLdWBlo8W7GSG0RoikcJF9UQsNB91x51ay1CzmevPoF5MinxPuSYWOWybcWHR5cfndBdhit9cLcezxWr7Upsw4AWBFVAKiua7Xyx3vlZ1n7WULVsNVEEoSDAQyzEdNV1LRY+1/fLtnIN0nE4gkVW024eEBRMMKGbmpsjlnrpEtOBMU2VFY6ObuapmHjxo045YxzAADr16wEAMvY/WLxRxg94UjHOXZjv5xFQPgAACAASURBVLCw0EWaZLQRf9cd82m8x8mXtqCgAIqioKxsj2vM1ZUVyM9P5RhMvdY4R0QwFMS4SUfj048/RFtbG3Qz2hwKhTBu/HjkZobRvUdP9oqDhgzDaJON2QjCONeleDUdQFGPnpg5azZ+PfdWbNq0CT0HjXC2MSfunUWv4oLvX4Q77rgDlY0x7KlvRUtLs6MvAOjbfwCeffZZ6LqOtxcvwV/+9Dju/eVNmDhyCGacdSY7DrtE4xoCQcOR1zk/H4FAANdccw3O//4PLGdZdkbI+nYGAgFPpwmd/71LrsCM82dhoK0sYF1LGyobYw6jkfZy+lR16tzZuv7s2bMRjSesmu2Z4SD6dMm2zpd5MwsKChAIBFBSUuLZJt92z7Nnz2bbiDV1a2pq0LXr/kELHpxhmoNcOmd1IIzSFNrc8w7iHMx9Jbquo7lNTSquShAIyyn3pHynk9ualxlGPA0Nk8htUkJPu5ub/+b3AE216gbbybNkhRSI9hjKHS1rSxvQ0qbi3HHFaUHF95okYsD2T4GCAYbjBIbCJsuADBgODj94qV3Kzchcr86p12yZSWokQ+4EGHBVWQgzANz7r/UA/AlqnOMxxn6aRE4tkDSOO0nmEI81mcinSESaV5c0SDsYaM57Ssw5YERI/eaE/sSVCfM6ryCH33O8UCZe/fiVovBi1fWKKnPXEI9xNXW586qanCiInTVuw0w8lsHMn2i4SkGmmfGsF6DbrQwkUXQoyNTHbS9hlugUEdvEmPxT8dpcOUQ+Yu2xDjzWVLVH+oBvvqwHxNOPPdmr9i1dh3NctWcMXAkaMq4qyg0jc/3qVejbrx8KCgrwr3/9CyuWfY0ZF17kOMf+XIcOHYp4PI7yPd4GBeBf4xYAcnJyMH78ePz7zX9CVZPj37F9B1Z88xXGHzXFu2+PvFdROHSHqum47OrrUFNdjVtuucXvFnyF+0TvKdntPGCOc/tmAwLbo0cP9v0DgGi0BSEhR/ifL9shtc47VBQFw0eOwk13/QoAsHbtWvGyrIRs+0pOTg6OPfZYrFy5EqPHjMXw0cZ/o8aMw4QJEzBhwgTzynyH2dk5GHfkZGxctwZHjBxtnTNhwgSMGTfeUZrKLjS+7OwcTDnmGKxcuRLjxo3DuPETrDGMNMfgcfse48nGMcccg4ULF3pGYu33PG7cOMeY7fdsl23btmHIkP1TBvJQZLcDRNP0Dq9Vutqs03bKociuS2rN+prHDpar82kXUrZkomokJXWtOHawvHeLrpGSDCcQAAZPAza9C/yqCNcggjkZGnL+ZLz2XXUdazJUZPw7AHzg7/fqrpltPw4An5pth50FzPyj9Lg7Su5/x2DrvWRy/44ZQLOR/43+yRp6lpEmCWGPxlUckSqSL0heVurtvazeZHmWjLwu3e6OPPjJ7Mn98OyXO6ThveSE61col/tKtWhl6yaTLpyfndq4r2qMuSDAXsoO7RkDu8lHsAfbvPWi0kOODU6p9Kr1mFCN70p5Q4w9LpYyStdwjcZVTzbmNUwOZ1zV2Bw30XDjDAqZ+qYZTBQ9HFQcz0zGySJjkHNTUpgTcRhx5Qz8W7StZCKyYgQbcEPLt1enJrYTjWbOqWAZfzo/FoAfc3UTb7x65ew2RPk8cz/HihfE07ekkhfs2PzJgRmIEZlDOnjV8yUJ2SJVwWAQ5513Hv628Bm0xRNY8c1SZGZmYc6cOXj22Wdx0aWXY+rpZznOt6/V4447DgCwesUydO/ZO9lGfIckjJP58+fjjDPOwE8v/R5+dMWPkRVI4K675qJTpzzMvvIaz/PYrpmDXk6cScccj9vm3oP77r4L3yxfgVNnXIjeffpiW6aCXdu3YtGr/0BWdo4vqpEbw/TjJ2Hi5GNw+plnY9LoYSirqsErr7+Bvy98Cueedz769u2LrQKChfqZcvzJ+PuLCzFp/Bh07l6M119/DSu//spq1xrXsHH9Gjw473ZcevEPMGjQIGyvbMArLz2PUCiEE0480Wrr5fwAjG9DOBSw9u7f/OY3OO6443DOWWfgtHNnoVtRD0Sb6lCyeR1UVcWCBQs8DXRN03HTXffisvPPwJUXnYvrf/Jj9OzZE1VVVfhsyVeob4nhhtvmuc6zj+/+Bx7EKSediGnTpmHWxZcAWQWoq63G5nWrkZcZxIIFBopC1hH94IMP4vjjj8fkyZPx85//HMXFxdi6dStWrFiB3/3ud457njZtGi6//HJrzMuWLbPumaSurg4bN27ETTfdJHX9/1YOGbsdIJqudzgT84ffGvXLBhUdKjskyrIdhkLfnrkhJc1OguSXe0QboyQ/kOOc7nnOCC2b/3TGQ8DSoYCm4uO1ZdhR04I54w8DAETbEnjxPztxzICuDigYJ5vLG/HJxkqccURP9MrPAja8DZQulx90B8qnmwwyjCHtKH+zV2T9m8bPPkkIW51pHMlG2bdUNruet5es2l0PRZFLk6CPbXYqTG87paw+ijxH7VlDvNb7ba8a5DSNklFvImviIlKcrC4xWNZlyQG7mU6rVFNZaxo9wYAcWGptSb0Fn/XrekDXHFfEzosMKqHpDuWbpCEaZ49Tv+K9kSElHidjKRRQXApSYU7EBRlusj1D+/N2kT9JkEhxextnEBtEPbqtzBB3nnAtGRIrxigTz7JzX9DpqXJtdV13kabtquVLRdmF410IKM7ryUSnpdirmWNVHjWavfLAvery2iOuHAw7HAy4EC1+UVoohgEqGtHkNOL2HQrI8WPUEQkGXBFj6kZ8rZ555hkMGDoS/1r0GrZs/BbFfQxm4o8//hg9B49yGf3BAEC2bP/+/TFh4kQsfu8dh1EsopG4rVN0Kp122mn443N/x2MPLcBPrpiNjEgEU449Dg/dfBeKPKDEXp3LqCb2eb36uhsxfeqJeOChh/Hb++ejtqYKWZmZGDJkCE6fcS7OmXUJgibcV6zj6iU33j4Piz/4Nx759b2orqxAMBhEnwEDcf1tc3HHLR7GkjmmW+f/GhmhAH75y19C1w1CrgWP/QUXnXUyAONbUNitO3oVF+M3v/kNdu/ejUhGJgYNGYbfPvUSxo0bb3Xp993Qdd2xZ44bNw5Lly7F7XfMxf1zb0VTYwMKC7ti4oTxuOqqqwCA3ZcBY/8ZNnI0XnzzA/zxkV/juuuuQ319Pbp164ZhI0fjnFmXsN8NRVGsvW/sWOP6d999N275+Y2ob6hHQZeuGDFqNH5+/bW2czxvCToMfTMcDGDixIn4/PPPcdddd+GnP/0pYrEY+vXrZ7E12+/57rvvdox53Lhx1j2TvPXWW4hEIha51r6WQ8ZuB4imd3zZIfpYyZYn+S5JpTk3svmUdiHSjhxT+U71lC14sGRECAA2lBkeTAsq7XeR/D7AKfcAAB5ctxgVkRjmTDNY95oaorj38w9w77ARGDapn+81P/tsG+5dtw6nnXIi0CUbqN8FVPozUx8IQjlzZ47y+cDva9nygfFzWFKBIcNBdh/IDgelS+B0zgq74JteCkuLmf+dDjR51pE8wQon/15X7vg91d2ePqIHXlq6CydK5lY/+dk2APLz+P76Cql2gDE3sogLyqO3E1/5ObDqW+O+c07nhphc0fzssOUsscuGskYUMYihHdUtLISVjJhwMOBYILR2RAg3GQy98rOws6bFKgkE8CRYXqkZovHAGWFiBMXehP7JBab92Jh1jzZlAmGVqumuiLpMRJYziFOVA+LuXYTks8RSuvu+3ORcwu/MfLmNeve1OBK9zHAQcdXtkMrJCKG1TXUZX17RIyvi6vE39rh5kqJw+az+JEKRYACtmkB+BD/WZZ7lnJ6JIowwIyMDF//4pzjptDNw6uSxePDR3+N7M419fx2TUy/OyhVzfoybfv4z3H7vA4DJjzHluBOxcletNUZynl99462YN28eQqEQAor7WRxz4lRMOu4k5GeF0bcwB3UtbQ6Y/7x583DptTc7DHAdwNtfrnKNcuLkY7ByV61PGpPzTqZMmYKhYyZYuaqHdc1BbmYY26qaLRJCAJh9yaWYcOq5zp6YpfK92ZfjvB9ehpyMEAZ2y0VVU8xyrFCJG/E0mqeCLoX401PPoXteJkpteeBtCRXhYADrShtQ2LUbHvjdE1Zu7KbyRjbqqutukr558+Zh7ty5WF1S7xr7sGHD8JdnnkNJXSvCwQAyw0HHHOq6juNOPAkrd9U6mPxpfQ0ZOgwP/uGvjgoadA8BRcHPb70DDy2413FN+/46bNgwvPTSS9azjwQDCAUDjkDO9Tffjqt+dqvLqfTnv/wV68saUNUUs9Jzxo4dizfeeMM1L+I9v/TSS75tAGDhwoW44IILUFgoh/r6b+VQzm4HiKbrLo/g/pYvt1RLk8x814Si3n0loZR2IUVKlnyMlNZ02JtJQemV5vMLBwPIaWcEjz4ssgYXSToR630hj36wCQAwc2zvFC33jrD3W2/mYEWSHxj64MqUqomrGhpjCSvKmLwWP7mrS+odsGS/lbjFZBy2Mzd7PTKCvUqXEWmHREIBhAKKb/1ZuzSZitMRKZAJJJ0yQ1L5vZqmo7YlLr1+t1cZSiSNO9Xrn9B032g6KR6c51/TdMuoduTqZQStCLNd8jLDLDeDrhvvs2gckOEaCQUca4GMHo6siSPB8iQlUlK3E9mYeTIqMeKnuww80cji2zj7aWDgvhuYOtE0JnoGMoRZG8qFsksSpX64afSrFUyni1BnDjIs9i0q+AlV498Bj0eraTr7jXAYVPbos2W5uvvi4NwA0KZ6Q0nbVM13w+PeS6/goqYbCAHu/mMW9Nn9t1BAwZqVBjnVqDFjkseZ94bmkS4x66IfoqhHL7z8zJNetyCNNZaKyLp+Z/rhor0u5IXUkHzZt0ns69SrNevQEN9rxgHn73JJLQlNczk4nONy/81y6HBrPK6y/SXP4f/mFWHWbdfRhePJ/pz3G42r7NhoHwrtA56TFStW4KOPPsLcuXP3et9ecsjY7QDRdN0T1rO/JJbQOjxv+EAVyv3IE/Ip/TY5ktK6VhyWRpR2twlZ6yEJUQWSBgpHvuIna0sb2g3l3WmVszm4wCCfmPWSTxiyb1mYPV9nXTdqHQ+ZDgST64kiuzLPkJTvsNk21daRmxHyrNEqyi7T8065w35dk4KXTg1cAJg+Uo5sCgDeXVsmnUMEACPNOsXDesqta03TMemw1GWKCB5dmCvn3NlaZbyTsmzlug6M9kGO0AzkMuWjVE33JNrh8roTmobundzjMuBp7idOBlpYMLRpTXEGuF3JI32WyxG190/CkRcV2EoZ6eANNUd0U5cjkeKuJSqobC1cJse7SXD6iLq1ruuuOcgXSjSx0db2wKyZMYsls2SIuHYKbMzeJGbexzmjjkoSiUq6ZZAw3YWCCmuG0HeYhTHrgMoYOXTvnPEQS6jGdYQ/kWHGGRa0ZjhDQNeBjetWoahHL3Tr5v/dEUeqQcHdD/4OmVlZnm14dmS3kHOS/iZjtHIdccSI7TGkuWNcG25piUY4dy9eUPNUJ6bhy0FC1dn3KAmt59YDjwIADOg89+56lYuj/lgDWaf0DWaNmyWJuO97IKDwufoqrf+9byaWlZXhqaeewqBBfM3mfSGHjN0OEFXr2Jxdqq87qjh9mO53QepbE9JEM6I0x1Q0tMpHvtoSJtQmjYipDt0si5L+GpKNmInS0c6Z9kpFYwwDuuZI52judakyIsuIOpVu+pDIlKiiiFo/iSgwYDg1UpalMiUdhwkR8Ph9iDlJp71IrpRK1pU2IOhTwkeUYEDxNMLsQpF3zsjhhHIoQxJliqJxFW2qxsIjSawalcL1Vc1giueWM+VWidKW0FgDZNVuN+wOADaWG1FML8OEg0oHFLiQQpzDRSybAsjBfzlCJBEWyrGbiyzjbN6qROkfsQ0XddwhRFI5ci5XHq1EjiynXMsYxOJpIps14M6fFhEAXkYtZwAlVA0tbXykKjcj6FuWK8zsQ7rO709WlMqjr0wGJUWGEHdOMKCw37dkKgE3Bm/jWYeOm++4G+8tXes8LmlIjho3Ed+75ArbeYKh5+7GdVCWVVlMF+Cft/sexfXPncUS6UlYu+39UrfXAJdJoyAJBBTeweGzHixD0yOwn+nzDeb0FqqlKwrdB7dfUDde6AKOA4BmKyLxTUtXTjvtNMyaNWuv9+snh4zdDpB1pQ0dWi6dol2y0L/vkui6jqqmWLtzmRVFPsoEJCNC6cCDt1Y2S9dFJSF4Y0F2+0pNrdxdh1HFnVM3PIDkiy0GMdW04fKRxb0ur11p/Jx0peMwGRT8R8Yp9abzRKbeK2AoiCnLUpmys6ZF2jDeZCIKZB1BpMiN6p3euklFlmaX99eXy9fwhMG8OrJ3aicfQbVlS22tLW1Any5yaQUUXfSDMZOhJJYeomgi50BoiCZYRWxtaQM7R906ZbBRFKqVKkYGKQeXW4dcRE+W2ZeDG7pK7TDruUuO0+jmFFSZHFnx+jLszFyJG3G+ZO5fBvrMG7LOY5xjQXQicEa8uL7EueCuHUuo7Bw1m2PglPq4qqML4zjyM1x16Lwx6ROl1a3qrk4hA5xT9g0iLB8oKdMfzRP3t7aE5uF8E6OTcvuWy7jhIoFSe6C7jYjS4HthzhPmizWSfaCxyZ5Tj5tzkMmY5Fwb0Uj3Yn33Ckbpus46P2g/Zg1Nc0/2gjj7rXHO2A0GFN6ZYR7iHER0m16waE7omXZUnGBvyyFjtwOka6eMfZr3lkrIwDru8PRL6/yvyx6TrMReWD4dWVvakFZeLG2A6RAEAemVNgJglR1Jx5CwS0BR2DIoB7K8Z5IjdSg5Vfk64+eA4xyHM0zFWOa5U75074LUxpSu64glNGkm8dY2VZr5mIxX2XeDoqNp2KIAJEpqtVOicVV6DROzsCwiQFGAlpgkdNxMCfCrbUzKeZ7g1KJn0F+YIzL06piodZecCHsfcVVj5zquauzYaB1yBnUsobmOc0asyDwMyBl8nDItKpZyrMrta+OXI+t1TKZeLqe0ivnPXIR4vZBDzOkT4vRz4xEVcJk5pXUuLgOCzXJcILGEhiBjUNKccUq417tK8yFenzOKrL/5RN5aPXIW6X74yDr1x16Ofa7cc3Sf5z4mvlfcHbpYvpk23PVd7SQjwu5Gcpu8GMlm82qFQ9wjldrHmTGJjjq/UXNOvbjKO1NIvJAIPNxdNyHJbvFztGiabukPznO817hfyaS4yjtnrDX+P2LtHjJ2O0A0Te/Qkj8E/5LNR/suyVaTGGV0n/ZFMbMjQc/C9pxQTlluGgby1spmVz5xKqH6h9nthDGX1LVK12I9UGSNWUu6QxEMoUxg0tVAVoHj8Po98vBbUlJkIOjEJC4LHd5W3Sydg0vvhiyqgEh9ZIx0u6SDWoiEAjh9hFzknkrtdO0kv+/JRms3ljdK7+mk4PlFjXXoyAwHXPlSdG5EMBxojQzt4X6Wmq6jDwOBVz3yKzeUNfrmKeYKz4fYVeuF9A3OIJCJmgJJ5IN1D+2MgMq0EcmnuLxeN9lT6vvgxizW4uWuJb7nXO1a0RnBOgNkSg+ZBgEpyq6av4ySTNcqEvLA6TgX+VpXWs/m0hJawEuf5oxGWoci9JiGzkXQohR1Zq4RDCgsszddWYx+A8aa5t4RGi9n9Nj3el3nY5pSx7ggqkRYk49WpjaSZQbFNpEYA9+1eNTdioUEC83aJAiq/CLLrKHp4VDxQxsY0WRvY5JzpsTi3iiJKMOsb/TnPW5PQivz2n51zfcFQVVHyCFjtwNE1fWOyyGEEbXIzw5LwyK/S/LRBpOJWTI/0i6apqOlTXWRQPk5PemjKNbS85NgQEFXJiLoZ94QBLWXJIGOXWgjPNgIzZZur8Xgoty05navSrQBiNWzf8qOBKXht6TsykSBKUo7WNLw0nUeAsmJl5LrJeR9lyVsozJI6bCMK/AmQhKFmM85siZRyNiy104FvN+xrHBQ2nm4o8YwnOzeea6Eip0giv5e0WgYS2KUhiImohFs/E1HmHkH1pQ0sN+h3IwQW26GIl0imzVBA4cK+55oIAI8eZBYRxiwlVWja7cz+ivW/uXJsMS8XrflI34rU9Wj1T3G42Y7lolspTasZci52H4EK4/4PEgao+53i64lrh1aM9xuW5ibweblEoLBXYebjEY+guUH/eQgydS/l5HE5RPTdLHwU92/3JkUhwCzmfDfhNTr3H2GXITWZfwxjdp7XnuFq7csJ86GrGkvQcgFmO8K04ECIINZK5Zjj+krFFB4UivzJxelTRKgua8VDCisc4hDbtiv5Qdv5vLc/aLLB6McsnY6QLQOJqjaVNHkKmNySAyhTas95F015sfeDrFJ9ZjXlDSgXxoljtoSGlrjqiP3VmYlEYy5PfDsMjMa0d6ocEcIQUW7p8Fyvddl/SLjZ8Rt7NW1xKUNUmIylYnsVqRB8EQfftmc2m9N5TlLskzWVpP8xg7F9VMCKXKcjhMultCk31VitZaJNJNRRIa3HxO7apYpSnetUXTOqyQF9zdSdMVILTk5xEiwphmMwNy8F2SHWQh7QtMxglkThBYQI3eEZBGVKfq9e6dMS2HlWJU5ciBRcRONMMBtTHPlbUTlVjTuAC7662riEpGJmeuHm1txjvi8XrFWsPv6Llg1xyDtIudi+hFOa4w5jVvOGKfvm4gKIKcHx0OQUDX0Yb49XtBLS9GWiOC5z+HzEr32FSPPl4FYW9E69zltqsbuCL4OTJF4jLG0uPcjdZzTPSfsOLhDwjGOMIuL7ss4aqWMZKYNt+Zc/bicg7q7zFg7r0/vlpfhz+nufkPW4e9M4dYRPQevYDzPxm90yCERvFiaybHHISjiPuW1DkY5ZOx2gHRkZJciA4d3b18Jmv91WbyxEkWdMpLPR40DjaVS55ISkA5EPTcjhCbJnEkgmZuVbr1bgsUU5KRPUEXGbnvzfTtCqFby7Mn9Om4QK83C6mN/6PrT9upmVrHghJaiTNkngjiJEFnuw02RTj+PsF2yI0HkpFEPmvIzZY1Aaj+gq5xDhqJOXCSOE1rHMgzUpLjL7NPNbfz76zWrlPvql4oQCiiIsYy3pkIjKDu0h4iKCa0HTplJaLoVjbX/Na5qrrJDQJLrgRQtOocYtEWSHKuEUSg5KC7Fg89tdf7uiJqa/xSNbo4hOSREx2Xq2sowNnM5smsFdmhuPFJlhaTaOOeD20vEMbcw61Q02kVDlTOiK5uM90jMmyRjg0M4NEQTLGQ+5gEvpnXOwSujcTWNqCjMfjz/5Jn/6ZsKovvnkYvvIcGW7Yc5lANnSInGnwyxlUz5HsC9brzqBrv7d/bFrb8Ylyeqiz+ZOZDIUfbqNtVRkSHaL4efc8Jpuu5hgBKqIB1D09uZ4rf0VBqDcF6bj3Fq34vsXdPts+M2f3ZkYE6WxE1GDhm7HSCbK5o6bAFR9OTU4d075PoHurS0JZzJ/Lu+Mn6q7qiBKBR9SMePUVLXipFpsBxThDY/Kz2jdd2eBuRmhFhYWCqhCEW2BHOwW/YivikN2WDCUCf2T11TdW9K/6rFwKKfAokYkJEHZOYDXQa42uVEQtLlptaU1nt4a91tt5vRVHt7r2gqRcJ6dHZGOr3W96rd9eieBgyeGMBly10RoVXvfDljd3uVEb2XTTmgSKBMZLdN1aTzdSlibCd78osEN5sEP9lMDV0SXQcO7+52mlFkkqCapMBS7qNY55cUdi5nt6IxhnAw4Brrlspmy8i3KxuZ5t6RLTg8ksREHjmcNoOTQx7wxlxSKdV1vo24z7JEQu0hn5IglhKNTQDoKhh5qaDO3PjYNsyYGwR4sT0XmNaEeF9idJwzfuWYoI2f4nsXt9ame6/aWd1iKdw0vnA4DDVujMllHJptOIhnMKCAK/1J88bm7CZUz31NgeIJWQVs96M7/8hFsOka4hzQPNq7kInQAvw6EsWPgMgaQzsipnwbuW86B00XI9dsXq0YAfdZg8kD3DhTj9F+LbG5m11eM38y4zF/eqF06LD9en7PVYHbqUjnq5rO35t5EWu/tbXhyOHs4/EqLRlU5Mv67QtpbW1FONy+CiKiHPDGrqIof1UUpUJRlDUdPZa9JTmRkKXY7W+hPJ4OhXcewFLeEMOxg20s1aqZ4zX+kpRwDoL7yNbmBAxFWZYNF0hGkdJ9fpnhoNQHkZNNpuEoUxP2QJFXl+0GkF794v9GyGA4ecPdwLJngV8VARveAvJ6s+3X7WlwGSZe0ikz7DAw/Awp8sL37JzaUCPl1wGJ91nkeVnhtBi5YwkNXXMj0h9LiuzKElQR5LKbZA4xfdC75qRO4VhbUi9dW5rGLXuf26qa0Skj5AvXjmsam6+104Tni9ei6I/YZ5tHCSMyUDn25kgwgOrmmGu/i6saOmeFXUYmKX9ZYeO5kSJG3zi70kt92vPPOeWRWPH92tiVYB1uGCMgl9sq00aMDMoYxDJ5xrVMDjER65GIpFaAO8+Zh1Y6ryUiGtjxCYf85ks0BkrM/GhuLJ0yQ+iSE3HsXEVFRSjbU4qAGnfXkjV/9YIxc7U/kxF/90khDwPZPBORYMCzdI1XII+PyOnsOTSNqZEiEgYpc8xtWLrbyOT6stcTTpPtReY8mS2Te9dEaDXXN3u7LqeKW7xsUCu/lXFyRCmqypwXCvClAC3YsUdUlVsq1Au7jsw/cn+KxjX/mtVe3XWQnavrOlpaWlBSUoKioqK90ufBkIT3NIDHADzbwePYa6IowBGStS33thDzJJeT9V0XUjxy2IhL6rd+hxk5SoflujGWcBFa+QkRruSnYVADwPKdte1ec2SMczlXB6pE41q7SMb+W6nKHYziuq+TBwaeyLbLCAWkGZNX7qqTvpd1e8z6vRJwY6qLKRvtX1fakFa5so3lPKuvl2wqN2Cyso4cUq5lidPKGqLICAWkCMvaEhryJNET28xoumzufSigoMkD+kySUHVX9A5IGrNFriZvVgAAIABJREFUAlkZOS7E+aa0FdHRRYrvET3z8L5Zossuo4rzXYzI68saWUgjwZdFYiCC+tqh73RdO2EW50ARIcAyRqpMOSBO6dxc4Sz1wxFm0Twmx+NqImUoitfnysF0zc2wEDwAr4iKBF688e38vT3zVdXkXSpKzA+ntcfVctd03VUuLy8vD5+XxDGwYSe6dwqjJZZAsMFw0rUlNFQ0xhCrCqOuJY5YVRiVJkJkT30UQQUIhwKIxTWgztgvWtoSqGmOW+fEq8NWLWt6P+pCATTZrqPrQFldK1qyQmiOqagNB9BoflsbonE0tCbQVhVGbUscem2G5TQqrWtFJGSgIhKaBrXGGEM0rqKqqc017oSmobw+hlBAQULTEW7MQlzVrBSAcFCBWpOJepvzO5oVQlVmGOWWEwEINmShOZZArZmC0poVQk1mGOUNUet7otRnQNOSKLDGSBAtFRHUNLdZZIRqTQQZoSDK65x90xwCQFMkiKbyCKqbYmg1a/0a5wVQbvJIBBQgUC+clxFEY7bzPK0mgoh4nnAvzRlBNGRHUNkYS5aXqs+EqunWvWSEAmirynCNyd53UAGU+izUtcQtmH5rZgg1WWGU1UeT9WOZvuPVGUiYz6U+HDBqHNdlIBwMQNV0lNdHrfVAcw8YKTX1rQkkaiKobmqDVpthGZdl9VFEQgEkNB0BBYhVGe8B3Tv1l6iOWN/tisYYdF1HbTiIZtt61cwxRLNCaIwm0BAJod5835piCdTZ+qNxA0BFXStqgwEoivEuJqqN9drapqK6Oble26rCKDffs6rGGOKqhkB9etUU9paEw2F0794deXl7x1Y64I1dXdc/URSlf0ePY2+KqulG1CARA+p2QdE1AO2BiKYvH5i5jOnk3n1X5KvtNQCAke10BJCe0C1XTlknBVTMI/ETisjkZaX36iY0//pwfkKRBd4JcODJzmoj+nXysL3jEUxHiuu+BnqMBK5cDGgqEHI7JagWLgdT5SQ/O2wodRJC77UMdJhyR2UjzJnhQFr1wXMzQtKM00CS5VKWAIvyP0XF30tqm9ukal0CRsRTtmQSGRqyRnpjNIGxffxJtQIK2FJfBBUV4Z0EDxS99xSRHNDVudaoH854LalrZaPOeZkhNhK8k4iihK4CAQXBgOJwplAE2g6r21ThNKoBw3CyG2cVDEGVDOSWYP0kIjszYPIm2NY1d+9NsdTGt4xhLUZtOUioVP1gCVKtSpeBnn7kmbs2kbd51VXmiAzjKl/m6stdrXi1PoozR/XEM1+W4Nv5pwMAPt5QgTmLluKeGcNx16K1uPvs4bhkTH8AwM8f/RTRhIoJ/Qrw6aYafHnbyQCAf64owfWLVuAPF43DTxYtw6PfH4MZwwxkzfUPL0ZRp0wM752Hpz8vwYZfGdepbW7D9Gffw9UnDMQr3+zGycOKcN+5wwAAD7z7LX7/0S48ftE4XL1oGV77ydEY1tcoITf73vfRPS8DfQqysaWyCf/+2VgARjWHOS8sxZOXTMCcl762xr2tqhlznvsY/Quzsb26Bdvum46vttVgzsIlCAUUDCrKxTs3jMXlTy+1dLTbpw/F5aMPw+m3/wuAsf9+O/903Pf2evxp8U4AwM+mHo7rpw7Gdb9ZbFVc+PQXJ2J7dTPmPG+kYJ0xsid+f9EwXPPCMry1ag8A4C+zJ+C4w7th+h1vO/p+8auduG3RagDA+eOL8eAFw3D8Ax9hh/lNffqyiZjYvwumz30XgBGxXz1vGl7+ehd+sWgVAGDWkX1x37nDMPuvX+GTjZUAgBeumITBffKT52WEsPruafjb0p24xbze7Mn9cM+MYbho/nsWWuaLW09CWUMUc57/AgBw1GFd8NKVYxzz9PRlEzG6OB9nPPseAIPPZNmdp+DBdzfgsY92Gc//5MH42SmH40f3fWChRr6+Yyq2VjZjzvNfAgDG9yvAK1ePwX+2VmPOwiU46rAuWLK1Bu/feDwGFeVia2UT5jy3GL8+bxR+sWgVbpg6GDdMPRwA8LsPNuGh9zbiqcsmYs4LS/H3qyZjjJlC9aP7PsAxg7pic2UzcjNCeO7yMQCAl5cac/bExeNx5aJv8NgPxuLMYb0AALc+9hl21rTgvHHFePGrUqy95zQARg3uOc8txi9OG4LHP96C88cXY+5Zxnq95R+r8Lev9+A3F47GjYtW4u3rj7V4Vi6c+y7On1CM7VXNqG5uw6JrjfX6zpo9uGrRMmsMvz5vFC4c0wcAcNlTX2Hxxkpsve8M/C/IAQ9jlhFFUa5UFOVrRVG+rqys7OjhpBRVMwmqfjceeGw8zmp+Zb9dOxIMpAUt/C5JhWnUTTqssF3nUyREFoZJ0Yt0Irv0QSuUgGLapa4lLg33FGV7tbFJc/UTD0T5z7ZqAPs/X3e8ssH4RzgHCARZQxdIKouyBuz26hbpNbK5skna+CMCEZn2uq6jIZrAqDTyy1fsqkurjND6PQ2uiKWfECFQkaSRGQoGHHm1fqLrfM1aTrZUpFd/eGNFY0ongKbzz4WMTbGUEBkaIqqEcnlFQyNhMSu78+oAvrZrXNUxgkGHJKPNQs6uqiNTML4J/WKHiHNke+LniZuvb4X6uFxEVsxV4yCDonCEZ6ITQYz0cmOsZlKVxPJh7TU4ZdqI5FOiEe2AgZv/XF/mJNkiWK49ak81a8VIPq018XhC1dAUS7CR5LAJmReFWvZm9o+EpuHwIvd+SGuac5aV1kV9Cbq4knwKFCiKR1kWna/YQI5rr5xdO/KCSqbZZ0VEk3DPlSO2EyHr3LdFpu4yVTGwP0MxN5lzGHJ9tQrzzeWnc+s/T9hH2RrbQl8cO3pC013vu3i9GnPtcU4/keSRItDcmNt8nId76qMstJ/eF0If2EXVdYxk1het8X5d3N8xqn8ucirQmLmxWUgbBuLc0qZidAqn7MEkB4f2mkJ0XX9C1/UJuq5P6NZNHmbXUaLpQLbaANQbXqcBic3757qajpK6Vkwb3mO/XO9gk2U76wCg3WWZKIKRz8C4OKGPHbfZeQltWOkYBRQNlo0kihIOBtiSCAeqvLu2DAAwtu/+3ajvCD9v/OOUu33bkbIgy4jeltDYiBonAUWRrjtLMGaZvGZibpaNjALGR1eGXIUkMxxkobteQkqYLAHWqt11UgYplffilGYuEkewaBGm6SUZoYBLqRV7jasaq1RVmRFOkZmb3k8xz9cyTITjZGCIkXq6JmfoW05aQegcscbv1soml1OVItKFtrlKqLoLii5uN9wqEpVizhkn5l3L5fW6r+WVU2qXbwVDUcaQlSLDkoj+cv2Iij6VMCPhSsqIjgeNMdKSbZ3PjJw+4hqk63BkU5qus0aj6lHmCjDWCxclpneAYxLPigRZ5EWbhZRyf7PjmuaZV1/b0mY5nOxTT1FDcXzkSKc1YeT8Gm162Malarrj2x5LuJ8RZ/yJqQAyhiXnZKB9pXOWcx3Yx8Q5wsjRbzeotlQ6URX0TO1LqSXG3J8wdJbBXWhE8Gx73xpT9UQkaYsxZbS86kjTcY64cL2ZPiTyadC+EWf0Jy+HI2CUpGRTRlTeeQkAa0vrWd1T13W0xlX2XWrx+D4AhjORW2sHq/xPGLsHmzTFEggryZd8SnQxMrWWfX5dKh1xKKjLC81Le0mN2hIaOmWEPGsGikIGTDrPY11pAyIeeYdedgV9gNtb7mp1ST3G9ilo17kdIVTfVYakaW9KNkxlsvhI33ak+MjkjtLHcoxkRLW6uU3ayN9iogTE6BsnpLDKGugJVUNDNOGZx8qt1Z01LRidRn3rtaX1bH6gl+Rlho0crBRCJY3s0Q2/d3RNST3CQUW6PnB5Q8xhGHsRjtlTTYidlnIGRe895Z2KY6ByS+L42xJGfwO7OR1gVmkjZk18W9boYXyQAibW2Q1aERcav2q2tRtiqq67DKSEpjnGLFNeyklYZR6TMArdsOHU5ZG4SJb9u6HrOkuQIx7j+hGdVVwbMd9/mwDXBlITCXHEiOL0kIFkdxokWbadi4rSXLrk8NHJHoyxGVc1NtpONes5hX9rVTP7LUu+A+6/1Ta3sc6oMvPbyK2LbZXN7JuZUDWomo6WNpUtMQQAnTJ4Z1Z3h2FrrDP7vahCBI6cjI7rC8RsgBuezxkpojOGngtHFmcfQ0JwcnHnEQrFbmyJezPpO/ahERrDvt+I672EST3YXt0sjNGYy66CE80+bs5RSYfsSDmORd7oz5ttvEuOm7gPSL6n4j4LJNM3uPWalxli+yPSP4uN33ZLnTL5uum0hrhSaBTJ5xy7BdlhlozrYJVDxu5+FvLUNUadC29o274nm6aPwYlD9n8u48Eg760rx2Hd5GCOnKwprWeUYm8tmXIm+6VBpJQZCbq8uKkg6ZXmBpkOXNpxzVAQLe1kcu4I2VrZjMFp1DreG6JUrMOQwG5s6XoifGg/ASQNRxn4MEGnuJqrnOypa2U/apxkZwShKHz0xNWvqRTK1uSlqLEIufNbqdG4Kh2VBgxWew6+5iWbKpowtGfqd4DGIEsKlh0JSpONWTn3PhFmmuIenTNd86VqOjLDXOTK6K9bJ6ejjpQ9kUndqoErPHsvKCoA5GYEWfbgjR6GtqabdXxtXSWYqIkYMU6oGjTdaWDZl79VWkfCcBSjWWLNUu68RmZNSUVbXRFiJpojHBNzioFkmgMJ5UvaxW7c6tBZ6GKqqIwMPJr6CDiel9tIAwzF3jBc3ccBfk2t2l3vOgYky3lxSng4qLAwXYpKFgpGraoZTN1cPWy63+5Mik8kFGCRLHSMS9Gg9SZyatBxOySYcxokNE2Ya7dhydV6FZ0C3PrsIpBa8td3R1810djV3edpmjEmuy6i607yQNJ97QYxoXLsjj3R8UCGsL20VzgQcLzb9FrZx7Stykk8R3NZxBj39vujfGFxfZNDgYUDqzrL8UDQee6cHHPP5mDpus6Xi7Oz7os9JlQNR/R0I3JoTx/I6ETkaOLQjHFVZ9MIDlY54I1dRVFeBPAlgCGKouxWFOXyjh7TfyNkqBTTizHmIgCAsh/qkb63zkjol2UN/S6JrutojCbY4uiykpcZYiE3XkKbf65krh9gwBiHpmm0kvIuk68miq7rKGuI4ggJI+FAEPq4TNjP+brBN64FANRn9U3ZdneN8dEU63Jy0mR6arkcHW7HyAwHWSVMZ1pvKGuUhuzTezFQ0hlERpEs+RVgRDzTIYfbWtWcNpmcDGFWbUt6pYTW7Wlg30kukkA1dv1yma06pMz7atRsdI9L1TRTofIwNAK8URu0IgRGO3punMMkoemOmuB0e6SwinByVXPDTUmRtSuS3+5pdCjXXG5csoaqfTwCdNODNMo6R3dDdzVNdzlYyk3Hjj3aJLISs4aiK/rrauKKvJCTwg4fDgcVR44wGbKFtjaicSszHtGIkIFZU/TJPh6Cp7phnhoLr6TUBA7JUpgbYcdO7x5Xxk/VdAxma1Abz1q8DOlcXDUBWkMcqZaq6Y7UHxolGRwc9LnBfL7unF23g8Aia7MdK6uPOtswBunWymZXBF5ReCPOPheqrnu0cRqtwYCztmpc09hxB4RoP5cy4HRYGeflZoSSc+mRV+sYN2OQKkoyz1pH8jnac/SzIkGHM9TL0SYeoz1CRLfQrXBO6pqWNrZUHBnObEpKU5tjz7Q/02hCZfd/uk+OsyUh7LfUn2UgM7D71SZhHleHtymWkEYrHQxywN+JruuzdF3vqet6WNf1Yl3Xn+zoMf03Qmu+Z3SL4/iA+KZ9fm3acAcx5A7fdSGFZ7IEORVnOADGxjGM8ax5yVZTaRCJXfykrCGaVt4kkISrFrejdBDBPmXL04iSRsrmXpE3VpYCwP6P7O5ZAQD4YsBPU7YlJVSmtA0pinaHiJ8NtrOmxaEUA97RVK8cWc5Io0gS90HnhPrl4LCcUMSTyyP0ElXTWZgfJxShGiSxLkrM3EZZxEV2JGhFoki8nlGVmSfnFyEn5Zwj1NlU3uQw8uwKDQftpMihaIQQfA26c6y0t1BUwT7K3bWtRj1S4ebaEhobqTbgj87nT+Wl7GMtyAlbSiGQNMooqqBDt8io7OdF45pDKSZHI7XRNB3NbarjnN21xn2T8cA5J8nwIXi/XwQ0GFCSkWaxFi9znrheNSZKpkBxKNRedW258QCwHpoI2W9PvjBFneyl7giunC/sX+v3NLJ7DeVlhhhlW9WA/gxpHOWSi9HgaFw1ov5sCSyNRamQY4fL16ecRW5s26tbEAq4I2gW6RszBlpfbmPX+Bl0GK3Gu2DfI6NxDdVN7nfBfh4X7RYJiMghYd+vxT2CyxndWN7oWhe7alod64JbjyqTH6tqusOI4iDSqfJqdcd5Adt5TkQSfTeCQmTZ7qxIOszchm0woLi+eyIMm+6bvsX25mX1UbZSAY2dQwmVNbQiGndD4WMJFXFVtxzM9lH5oSTWljawDk9ysIlODMAfXVbfGk8reHOgywFv7P6vCb1Q3RrXGQcONyjF+8e3eJ2y12TRylLpyMx3Tai0xbi+Qm7qc+cYPxX3R08Ur5wJL6H+ODZSL0moOnrlt49VOZ0oGwnliLTn3I4QUopnjOm1/y4abYACHc8kTpFqTsaMDIyZjACZdGvaW2RJniobY9I5snR92XVAZDi98+UMRjKy0onU7qptwRhJtkh6v2WMaWKplkVc7KpplWatJIXIjxWa9ErOMC/ICSMnI+RSkDaUN7IGNkXkxD3GC95MClZmOOjY72htcTDzdaUNTviiqZ6tLa13rVuaUzsUW9V0Rw1wTpkmw4oUVSrbRnOl68AmgSSHUi/sEHOKpEQEQ1aMeNnHR84FO5SUrmWPSsVVp/G9lYEo268N8NG9hBWlT44HEGHdPKyamnB7gBeDs33dUI53cizuayfM+RHXW+essJW+YBdSmLvnZbrOSWgaq7iTk0Z0lhHqgnMkrCttYB11KjPHJJQPyt2PpuksiVOlz16SEwkhaJbcsgsZtvbD9C7Y381IKOCIWtO3wt6fpjnhpbquQ9edkfPcDCKCi9hg/07DkkoK2qH9nbPCLge1aNTvMdOv7OdtLm9yhQBK6lrZNSsa3OK9GWOF6zy73ywhRJu5etBGhJiJktvWGzn9spla4KKjzuImYBy+ORkhlgDNbpyKqy8rHHLtv0DSOdQpM+xakzvN8XJOnfzsMBpa3foncVBwSKWEprOEW/QdkEGfHSxyyNjdz0IvsbXwBhsK8sToF0DJsn167U6ZYRZOcUiMMimAMUeWvH5N8t+9xqTsI6FqGM6U5vCSNjPHSTb6BRgfELEUQCohpsDsdtTJpXxfsWTGgSorzefIwd/2mWxbDACog1w0ef0egyhE5jmS0iqTP0ofVo7dlJPyhqhlNKSSjWZETrYGLkH0ZdccKZCy+xMpeA2SziWKkg6TKCdE0TdZiHdzLCGda0y5z34QaVLYOQhZXNVZoh8v8q24OZ9i6RxSVEUCkoRH5IAeC8fS3Dk7zEYoCnMyXM4/VdPZiIkjauNhYNnJZ1qtnPCkQ4KijzRvZOQU23LpEpqOTjboIJd/qLrGA1cbjpCnojHmUNJzGPixojgjRqoQbSJiKy5vkztGQk4UmjN6jvY5o7J1JFEmGiaKphuQUnuThOomFAMMBZmD87d5lOPRTGQGV5aF8m/dJEHGfXH5jPnZEXb/oG8YZ5zSmDjyKkXhnW9kCAxiSIcSmsY6r8mYsqO4vGC1mTYUFZW4itvQXKruhKtyUGcuktwcc6IcyPqy59WqjPGjKM53iAwtu9O9U2bIxSUCOGH7ZJDaxyDW2Kb3wT6GHaaBZ1+nmubsh/5mL0OnCbDeenNfb7KNiRwO9rxZVeUj9/QN5PS1uKqxhiEFC7j3RfVYK3HGuUZC9yyWmDP601lOCnL0cjpEW0Jj33/6BoqM6wezHDJ297MQlIL9vHx83z67bixhkL+cNuJQ2SFOCDo0rl8+sPsbYF5nYMVC4483rgdCqV/67dUtaeXFrtvTkIrLyCUBRb6uKAntZe2BIpPnO53oc0fKmpIGFBdkSTEd7zXRjY/Tv9RJUs2p1JRYPoaTPWaEVKY8FX1YZWHu4WBAGtpOH3gRIu0lFNmVLZFF5DGypFBk2I+WZKkm1k+vFAS7lNYbbTsxxCGciLmsfuIHayOJms+PJVxS+XIoqqazkeCEakQJReOaSI9EhY5I88RL+5W8SKgay9Id1zQXKV5cdUObXQRVjBFg5BLC1aaz3XAUDGkvIh0HtJKifgLckssNthtlBoO0HTLpjpKqAgRV0wwHjVNpFyJb5rzbjxHUVpwj+++0T9O+x0FGRRgvKcE0HAt11snJTOsmotJY/SWu8jm7u0xor/gXivhyPBmic8N+HODXYWubytbfJTIrzjgtN41JPj9S9zRsAN7o2VMfZY2HOAMZtsorCbBeext6b+37tJgLS+gfe34qB48vEcgLyalk1wu43FvR+aNq7ginqrvr0iqKE53CnZeXFXaMm0MxkFPYDqUvqWt1rCfuXRfvhb6Lds4aetftt0wpO+K6J8OYQ2St2l3v2tcA/7VS09zGl3ITcmztQilpuYw+4OWEIuOeI7FbW1rPfhHTTUM6GOR/504OEqFNKKImvazzCu43/rHp38DzF+yT6370rUFOlR2WJ0P6LsmSrdUYq2xCz4d7AH85KfmHOR8BeakhsaQocIyPXpKbEUwr/5a8/rL1PEk2lDVKGymikKIia4R0pDTHEmhTNV/yn30ir14JANAkt9M1HvXwOKH9oigv9TOn6E5/CQI6XddR0RiTHsem8iZkR4LSpE0USZWNsJOyLgsdJjijLE6FlGQZWDUpTDJRbIroclENTjaYMHtO8RCFY8IkQhFR4qrG5hBurmhi4c2kUItl1qitGD0mpY2DnHIGEWDU+BUVti0VTRDtitUlTrgzleaK2mqMiuWJksq8s4SRXeFMcKRAggJM92U/JpZosSK7QjQtwBip9mcmKtJWFDfgvJbdqUHryY4U2FljKN90Fimi9vM6ZYYRDCjWPHKQUfHbQY7Zok6ZjtxI+1zsqHaTIa0rbWBzzleXNLAcDbRmvCDz/QvdkP6mWMK1TgCgxoQVc4bFutJ6Ng+Z5pKr80uRR67EypbKJvY6Iju2/YrNsQTqWt2QWpH9XEcy4ixCbUXCKMD5HCsaoo41RHvbYTZj3iKRsuf6hoOO/Z6NLOvucj0i0sDbGeX8XdfFqLXx0x6V1DSdzavlnF9iIMGxPzDv+pqSehYOLRr3ooGYae7NonMontA89+0uORGWY4HWOBelrWiMsXm+hHKKa+61TPfHOfNL6lrZ/ZnmlAtY5GdHeEePuX44FNHBKoeM3f0stBcP3vV34x9KEOsyRuNXhQuM3zf9GyhdsdevSy/QGaMORXZd0taCyaXP4LWMuclj5z0JzKvH/7P3ptGWZFd54Bd3fPOQc1ZWZVVlzaNKUkloAAkMMkhIskwLoZZtBjOYBtyYtuWGlt2SoZsWGGzAgAEL1mpswMbLNm6JQdaAKDEISSCpqnKehzfmG+/47r0R5/SPEztinxM77j335cuxaq/1Vua7L27EiYgTJ/bw7e/DoVf57SJe1CTq9zwumuevbA4Fe6Z7mA89lQ+01uxuWzuYxn47wJgJpvemh/bcuIO2N4DQOOdntV+f8GS1lFRrBhkFRyMeVXmqXvoEUjRffdiJARMA+o4ZSKVD8qrXboU1kezwDHapD8l3Xl6JoXDjHrDqr1zZzIWYu1eLxuFDfAWkTrXb38XXCEqccQec/r5noipWwoxjnnV08mSR6L6759mNt3UD/aRPsZs99rGEGMW2+c2tjMM2WilaBDyAQQvwXk/6Bk+wzW+0bSit5KhHabVV6zQBwZ1Z6uknI2glh7jOjlUseDxpvhKTNhBDJJ0+W3Os9PhZSaVs9clNBtHazgn2pkfLlhO7FV+ru1jwGGlt7SsNrNN9R04vsHsN3Uo0YAIDFxq8Z6IqzrU9ExVRW5jLpUifS1XaCytNMYFEVdqK8J1dExWxl5Hun1RxLRUCTFRL4vmUCwVstLKBKx3DSIPZ36uUCnhIqC6fXyXCqHR7qrjxYZ1ZboiBHv+s0YksCTCpQkxEWS5EnLP4SoH02Zzjiz3kzvwvOsgHaRt3X3l9tWIg7SAQ7t/Dq8bInO/eSXutlJJf7jMKmOspJXbnNtoi5J72I5FiUmJECihHK0Wxz5cKIHcLyc5IaZE3g66J5BvSGiw9Z1++vCEWMvoheW5XeznYvcFGWZZOZRew52GgZF5QR6uvBN7z781Gv/Zm4DffBfzn7wR++9uAn38GmP8S0FwBOvWcPfe3T8WV3QPTd45u1rZs7Tyw8DxQWwDOfBL4xAeBnzyI7w9jyPKb/3cT5D717qF2u7QZZ+Mcx7JfIWxypGz1jwwycsDcvqhBy5FLeDKMUbDlyorcivanp68CAF517+yALXfQmuaY0Td+2Luye3yhhic9kxzjDqFOPyPncXp0cGKD5qmvrnSoZA2/PKPA27W854GgY5KkgmTLNfO8+cLr6amc8ag0z4yWRedXsqV4HL4tAtSvbVUpnENR7225mCXNCZUW71mlVExg7NwiLZNhEdmKe5rnYxZXt0JADq507OmxfGI+6lulYF1pjSedPkitgQf2csc1Gxz1IpWen04lkngl7+iCqezRyCmo5hWg2bGKpaNL37+XVRcjpa01lhw/3rd4cqlhJSio+msTAHWsbYjsqNHhFSlt7Vdino0cSG8C2aQqoTYOOp9S9F5xCYIkMqAk2BUCDa11RqowVFqU8YmUxv17JHkf2dkm+Kr0bpoeLYtwUXLcpSRXGGncI2idkh2YlrVEpaouYNaqx4V1mp4dqWWpF8max5Ss5Ot4pVTIrF9jlaJV1ZcC0pFyIYEMay0HcXTbLaivc/+JP4B/NlYpWmzICUSaPd+S9FSzE4k95XKQbPfn83lBzzU4+s0xAAAgAElEQVRfT1w0RgL1Zyd3cTU7JqW1Ba2WntHjC1mUgstsTdYNVa6uexjJTOB0LJenADDXQ3p/XUoSszLDvQjhj4Pch4We+YQhXEApzIyVxeTp4uZW5rPb3XbUgw2CYBTAYa31yZ3c751k9BBrBMD+J+w/PvQ3gQNPA4vPA+f+2P7br31t+v+x3cCjbwcOPAUcfj0wtgsY3wsUcyCJa+cxps3kHRi0dOpAUAAqnqzNjWXg+d8FGotmXJtz5t+gAOx+AHjiW9L0sorM/lurQK8FRD1g1/3A6HUITlQE1BeB1ooZU68F/NGPAc1lcfMvqofx3+7/EP7vr/vmbR2OoGVS71qe9SIlvkzzbDEhtxlubCuNLv7Go/uH+1JstLD6wldvmEU94PhHgcufjz/QWN18KwDg2XtvkMZuFAIf/Ufm6ON7vb82Ui5iw5PQaG6j7V29pJeWDzSZKhYSTE+yF+Y2h0qUVUtF735dbrs82R/p5e4SL+UZVeV8GLCPzte8Wespk+7bJkCVZZ+ecgnSbeDK2XPWWovEZGGkxMCdSF/c55qccbdVop/kRRhpPH0ou+4FgYHBcdin0llH0u1/lapJ5WIBjx6YSiDGVP3gZFN7Jqq4xI7FK8Q0BqU1Htk/ibMxO67U25hXkeUMzVMjJbR7EWZg5hMhJfi9afdCWys49imP7B1PAneCjdIx5KqZXf2Stom0HRAQpJI78ko5lV5Hu5j6Od0KeraHU+7NlSChQD46hd6ZErak2Q1Fptp+jLjdUO5n7/edM8v13ERwqLQYIBAKIa8iLfkASmtMj5YzVcwRYf16eP8k/uriOoC0jchNUkwNgCMHCCziKSALUaah8KBKA3jy0HQii5joq++bwOcvrJnt42CeEzKdX2lagTwRHPHgkILrUiGV+dlo98S2ggf2TiTb9yJlIYGkoJkSFjx4dNsrVhIIfPrZrvFKBkHQjZTo7xQKgVi97UVKZCE3fzNjlTg31hqyNi+ZFCCfWqon3+FjTKDPofA0xR9J7xOltJVodL6y7fa3W9F2LNgNguAdAH4GQAXA/UEQPAPgx7XW79ypY9wJRs6RGDuUR4Dv/yywVQO6TWDygNnwc/8WuPJFYPk4sHzUBIt//f/KBzjydcBdrwRWzwCbV8y/nRp+GQBGAHwIQGnU7Hv9PLDvcWDjMlCdMEQ7jSV7f2O7zfFmDgMbl4DKJNCtA3sfM4Fja7X/Cf+X7wbK40DUBVQfB3/2fhOsTx4w5xlXzJ4EcKJaxkjQM2OfvgeozQFPfSvw4DcAhRIwMg1Up4ArnzeB7ed+qf+YXveDhnCqMg4c+TpcGX0I7/6Xn8X37L2///f6GLEmDlNAPbPcGArGTL1AUo9TnoWRQqR0IqcyrH358kZfmZQbZusXgQufBc58yiQxLv15ZpMP4lfw/dUZVIKz2BHQSnMV6NRMQsa1C39mUBdd48jpR98O4FNeu+0p5R2QNzthRsOVzDgM6YQjqRPJ4XOh9CRRwB34ZFvhWFMjZbRystqSHVuoYfcQveVX4p7dSU8EAVVUD3oG4KdiqRif4Hi8WvRmeaYsvJShl67juatNq5InGTl2EqT74mpLlIsKlRYD0XNXm2Kw68LxaH5Q75uLJCAuAu6c0TgvrbXwuiP2fE579rLQVQk2aBEyEbOwE/Dxe0cO7zSH7iqNB/ZN4EzMmuoGhRo602ub17fokga52yhtCMGanRCaVZpbvXTelIsFHJoZTSpvSQXOrbYGAcL4Wkrwz2w/JDLbXFxtWs4vnfvB6VFcXjPP1onFmjVPyUGm/VAinlfLldaZxMz5laaIkDi/0sTTAlEbzWM3CUeBwN2zo0nPPtnxhZrIhExSMdKcPpcTaEoVTLLRSsnSeCZTShtiJvZM0WWhMYwLAUwQQERYuFI5dAwiRkufP51J8gB2ks6dn5QE7/Q4ZNdOPGitM0kcmiMTrLXDPb5YodXZYHN23ATyFNwSG//hXeP4woV167x5L+zCRttqmZBYpE8u1q2kUWZMOq2A83efey6yzFcWKXNiwe6j5/urlvn6ZyzRkc4h78v7W70TWn2+2vmOVNkdqxRF1BQ9S/cxZAWtz4u1LYyWi6xfPD23vCp2vzHcrraTZ/IhAK8F8BkA0Fp/OQiC+3Zw/3eESVnZjI1MmR+y1/0v2W1aa8DlvwTOP2f6Bk9/3Hx27o/tqvDsfVAT+/GRpYexZ2oM37JvEZjYBxz9PWB8nwkidh0Bdt0HVKeBzUsmqN3/JLC1CaxfAAo1oL0JlEYS5x5Xj6fHOPx64DXfYyDCQQB81T8wAclnPmyqzsWKCWQLZVNhnb4HqIwB7XUDKYY2sksjM6Zit/uhJNgFYAJdss3L5t/n/5P56Wdv/GFg3xPA/F8D93wVcORrzXgcW4gzlr5amVIPLjkgvuy2gJkDHUEuJM9WYudkmAWIXj4PePYUujY5Uk5IQbZjfh2hsW3OmSREpwac/qRJvpz9lOljd23mMHDwGQM73/0A8Cc/Bfzpv8b+YAP4id3A/7mOoamutzZNMqdQMAmXX36d+bwyCRx5M/D6HwQm9pug+6M/nH7vB/7SzHHk92dzu7zWxuuP+GVFOqHCow58OO+b9NJyM/pSYo3IUe6azspMSHZ6uYF3DaFdPF4tYr3pV70GgHaX+tf8rgtVhVwSkX7jAeREgGuX1lr4xif8uA0oMJhyKsZ5ZxEpnVQ9cvcZLwnSWN33Bk23M8sNEWZeKRWwsJl1jhSDp/I99pz+P5rPUkDAjcMODbRShq6agMRei+qdUNT/tGC6Sme0efk46TNJ19Pexm7pICeZB//trgzJLBayjjRtRQHAfbvHk+AyigP0MO5zTqrI7JrUtkLrWJTY4tfj/IpJWFA1rNHJQpRLhQKuNjooBMglm5oZq2CV3UcKSAjtRedwFwuQwyjrDJeKBQvqSlYsBGKQ0FNGYo+ebdqCzlWa57vGKiK8OIGFCsmlSqlgBSNkFOS4zyhg5gNnUqf5zuWK3DUpZQeWUSJSwC/1hnYj0+fK+37zAlJ+LcLITkDQ3D28awx/cW41OS++n1UBjiySQTmJp7weWiArdXRwdiRJLND3rOAwPh73X6ZGy5gaLSfri/vMam2gvhwVQOSEXEs6HWfyES6utvBq1tJE+97jEGQlx4o/2z1RRcUhIQOMVrKU5CFUh9RjSzwi7npK18IkPu154fbYcp9ifmMrJ9mZ3/pSa/cSJJS91qtcHg4JXXO7204Gu6HWevOWgzveYpZmTa/xOo3tAh55q/nhFvUMtLhUjeHEAc4u1fGT//o5/LPXPwZ8zRGz3bt/Y/vH3pwDNi6aCnI5p0pRnQTe/evbP0Zsx89exDv+3ZcRoYDzP/k2oFA0AfjaeXOMTs0E1uVRA10+9Gpg8qCpkpO94tv6HuOPXlwEMJjspt/UpspHueQbxESIlM5Ic/jYMGzM9PIZRhKJ2/NXNvCWx7YHgRatvmQSNLU5YPkYcPAVAALgr3/TTqC4tvdR4NXfBTz9HjFhUf/qD+BVn3wlTo98u/ngx2eB7/4EcM9r+49HKeBX3mjmVC9+we1+CFg9nW7TrQMnPmZ+XHvnLwL7HkUg9L1IRi85iTRIsrNXG97kRyfj6qWPfi8Fl77SAkGA3L5Myc4uN/G1j/hDuwGZfTjPSHfSt2f33NWmN6x6q6cGBqRkFBRIkDPJOqHCK+7pL1NEwUxeYH5415hIjLMukOloaNk5EoIYINW9DILAWvDotXXXzGgyz8zn5g8P75+0RkT9qe6cqRQLWK51kkoVIZ349a7Eju0edm/n1ttWP1q2apsfKPBAtrEVWqy8y/UstPHMcsOC0FLLAY/bIw9osVsVDYXxzG+00eqESUBFUGJeXa2WiliuNZL3E80PcnABc6uePjSNo/M1azw8sFZa47GDU/j8+bX4fMzfxioGki2dw9H5Giaqpcy7TwrotNYi6ujUYj1xd/i8Xe9DHBUqLa4HRKAmSa90QyWiJqiSJaFGeuw54GMjaGuelm6xkJXzAky7hLSmnlpqZFBfLqu11jpTaZMSNicW69Z1ThAD7F4v1rbSZ0On++Fw5FBIiLjPUFPoq5b1gVVGUivve+5zs2eigtNL9vm6SAuu6Zvcm30T+POzJrhXWiMI7HtYKgQWKsqnRYC2k+ZeqVgQq/ZEUiit1wQdDgJ7vhAvgxQ8n4nXV54gIqtt9URN97WYn6BUCDIFhlKxILblUEW6KFSdz8RBuk9y+HaxnQx2XwyC4H0AikEQPATgfwWQxRq+xI184uuWEiiWgelD1kefi7N9khD7tmz6UOYY18uikRmENE0LsZMye5/52aljxIvga+7bfq8nwTB9WHOBFEI2TG6I+uwkuGKekTP5wL7tQZErxUKi+7kj9suvA9pr6e9udT4omCTGO34BuP9NBqZenRx4oU4s1tFDCf/uq5/D9/7pm8yHv/4W4B0/D7z6O/O/+IfvN0E3YPrgq5PApb8wvx98Bvj7HzfIgN9+L9Bhsi//068PTWIGpJqZD+33C2AnqiVv+R4KciWiDNco2HarwJJRQDMM5D7SWnwp59nR+ZoIqc4zYtX17dkNAojBoGt0rhKEUjKqjvmiLY4v1PBVR/qvM+VigB6EKq4ARSQLIDPBh5FMaHJ0flMk1ZqsykF7PdFdtPdFDqy7JlHv2IP7JvClSymMMQjMtSXSKKqKcEIoSaKnF6kE2QLI1c3lesdiKl2smTWZX69Lay0cYgERxRp8Hu2eqFi/UwDKneJOqMTAmo/HTSiQQ8q/NzVSQrVUSIJ9OneLcTUAnrp7OoGr0rHumhlNyILc/sS0H9cOQFzG3EEsu3smq9hoda0EmiEVymolKy3DNSdGSn2luSTd+G6OZu+p+Ll3A0qq8nHGbLIwMhJQUp/8sfkanhEQXZSkkQLXpVpH9N+U0mh0QvFcJ0dKGaTLnslqomsNsOprxyZnAux7Mj1atkmshL7zdjfCaqOTJG1EVuUE1pue48mluuUnUrsLhxFTXzx/JW/1lBU0SYGtUiYg5bfBTRqtCn21bpuDBPXfbPcyqKogAB7aP4lPHl+2vsfh3eeuNjIIgjwpNUBO8qTw/+z2YaRE+R4KmqX2srEqwf6z7/1SIcggY4CUw6JcLCRrKh+D1DPci5MkUmCfpxpwO9tOBrv/EMAHAHQA/A6AjwP4iR3c/x1hvlIfO2mLMSzsWoK5O9m+fHkDeydlOYVhzUcPFUiDXVeIvZ/RYi4tXHlGxCx5lPn9bKsXoRMqMbs9tK2eBf4Nk3H66h8BGleBfY+Zn9KIqd6O797W7j8ds40/+/Bh4Bs2gc/+LPCpHzdw42f+LlAUrlkUAl/4iPn/+88C40yyqNtMSdrufQPwY5e2NS7X6L5HnpXd1WZXJGqRrBsqUZZAMgr8fDK3Ydx/OWyW9ynBMcizyZFSkjDyMZIL8UUShZHGK+8ZTIQ3LPFVfSvE5EjJ+9pMjpQGkgRqLScsaGxSRdbtLyQ7sVgXWxhmxysi42YvUrhnV9b5IYTIqLP2kGOf6c1NYHX2mNz+U6mPTqoKFQsBHjs4hXMrpuKQVAXZ/utbIaqlNKFRYJVLspmxSqb/kT5PPlMaB6dHk+oGARz52n5ysW6hCqRA8fRywwoSe1LAoQ1Sh4JduWqmUeHBphDcKKefkHrJubkERdypp0SKe2ylNI7sGU/uP33PnYN0PyT0RxhpEZ0S5pCeqRjqL/lKlFRypbGoSiWxhZ93+pm5TY+WLTgsGRH4Sbq9zY5NPEZGQYaUnIyUxhOH0mSU1hpK2UzXSfWVvW+JSM1lyH7kwGQCWU40ddk5VktFPLhvIgmcJVZnShjypca0hbBqc/x/HhBJa92ltZaFUqMedn48STe257QVpIgEWz9b0h7m36PWB25uzy6Nie9rtFLEcq1jPcsr9Y7IqB3mkAPSWndIQBXkJRuJiOuI4FudWqznorMipUX/j4LngzMjGR3oK+ttkZwqlUITNKtj1QDf9+DtYDt2Jlrrltb6A1rr1wD4KgA/pbW+8/irr9FoUt5ItPfHjxqMiA8T6UvRLq7Kmn7DWFpp8qvsUrXAd3sAuLDaxK7xylBBORFn3TsEqRUZZY935zHkdhry59xUBKycAf7Dt6Sfff+fAd/wIeBdvwS84YeAB78euO+N2w50ASMgDwBP3BUHWF/zj4HDbzD//9Jvyl/6/K+afx//W3agC/izkQ9p3T4vOdeU0uiGKpkrg+zCatNbF4/0RX0qkgT15BIu/UxrM+7qEMHx6eWGyCacZ5fWWt6BPQB85cqG13PjasC65vq+F1eb3glMpTTWWz0cmskibDhpSLMbipWjdScgSsek48Ale73HK0WR4MyV1yFbbXZFBlqqcLlwbbpeHWdu5HFTuNIZifamVBXiDj7sqjLtl0Odq6WCFezQGHhft9Iae7mMTyLbYo9d6lvkTvKeiarl9EtInanRctL7CaROOl9P83qIuQPqVpqkgLjnVMiobYW35kRKJr6ir9G7gps0ry6ttTL3lRBEjwgEUYZBPDuj6f3iJnao4iS9G0OlxF5ZmkdSn2+lWMh9RnuRLKlGm0vPaikmHiMjNEgo3N9kfJFhduZXIY9lm897gl67yQ9+Pak67FY/eUJAknmaHTfX0U2Ec94R+VnMar2OlotWgJayk6ffO75Qy6yfl9fa4HlfOh7vq3U1xKUxjVdL1rzQ2iANrBaG+BrwQFbrLIpnrdkVUUkXVltioJ8w1UvIgYWa+N7J4zQAzFrbziEUrW2F4rNE7wYpkWrJtjGjKrqreW/2468acLvYjgW7QRD8dhAEU0EQjAM4CuBkEATv36n93ylGmcIbOYXqWz1USoUdqVzeidbsRiI8ZRibHDF9TfI1zi4mnfhl4AMjTfai+0MxJXKkdkyOssdT0oUbVdokYg/02kDtiul1FewbC5/HhZH34TW/+SDwi69Ot3v/OeDAk0OPZZCdWKxj93jFhp29K2bl/tiPyF/6+P9h/n3rT+/4ePIskV/wCEq3EkZL//aDtQHBGhlNUx+INAXbvrJaFEjnkV8AwlwdMLdd60b+fbWAIbxx9RQlozHc5yAu8ipDY5WSdw899fXzXksg+y4oBkFmG40UDeC2o5BjJgW1Smd1uQHjoEnB8VJtKxkntzz5FgpMXIQKzQGCzdKVX653MvqcgO2UnqNqlodGrOWE67TqQczLQJaAxyafQua83ApoXj8ud/jpvW7JuGht6WlL++n0bDg0OaX8s/Vm13JwCfpo9dbObWZYlIG0Cqe12Td/PZ1YrIvvDO4wK5Wt4gZBdq4lVSJhXTu93BA/p3Mdd6potC8pmWV6bPNJqKRAoBcpUVFAa52Zj8l3hCQM2VqzkxDe8Ye3n8TRWrObZWPOgZG7fa57JiqZIDlhcQZQjO8xv2+Rk6SgNcJlNXZ5DNz7ndd7K12zA8LzwJMtu8ermb7lUiHIwJoB+9l3tX+TqrGzPliIDeFZo//z5JfUFjJWKeYy5m+2s+8oKdlANpOjQU7vLmmu5CUiE34D9uxRooUOLUGfi4UAjx3IJnTovS7JC/UijbuGSCbfDraTNerHtdY1AO8C8AcADgP4ezu4/zvC6OVyozImWmss1Tr4Jk920ZeatbrhUPDPPOuFCgeF/oa8u3w56b/1fwSVlslm+qEESJdycmT4qj5Va0TN0QffYv594T9n/9Zcwa9Wfs7+7P43Ae/9nWuq3vazq/VOlmBs1xFD0gYAz/1M+rlSwG+9x/x/bLeRu7pBRggCHxZhqppJkCrJ6lshnjnsVx2lSrhPAoxE6X2YpoFU41MiX+s3V4dJOHVDhScP+fcQ95T20s6lDLlvYvDo/KbYkyVZhxGr9DONFHrLCVfyoMFUBZAcJNfpJTu+UBN7/8erJeybzJ4POWxuMJEHhaPKTqWUsoOS48x7FxuC3A057eOu884CZ7EHkbRmneO5MkK2k2zGyYPty2vtgeRTboU6kUJiDncusy3b9+nlhhWA0PrA+9eX61uWw0zSI/ya752sgudHUgmX9LwBOwE1M1axKkj0Hf6umNtoZ56FANlnOyFJEp6b8WoRK/VskCDJ6ph9yagAwFTKpEeTWrUkhNaxhVrfipwEVV7YMPuTUA4LG1ti5Y0SPNLfrjY6qHfsBMGx+ZqYVHGZwN252AllMqhMsMuuE50ir0hLJHV5KAJ3TBmtbGecYnuC1hm1iiBwxkT33pF84tXXjlA1dpnYXQkk2sYdk9T+EWmdSfDrmADrMQEFQCb5WN1QiVwXRPwlzWV3bSEj5Q5a5109bCC/xUVCPJCGs5TwDiPlxf1xO9lOnk05CIIyTLD737XWPQypPPJSsBtd2aXqnK/D/FIzIu+6f8+19aW+MLfp3T/IzZdNFjCMsr7suWQ+zLz9jgfk9G089g7z76f/L/OvUqlmyke+AQDwK+Hb8YXvOAd8cAP4jo8Cj75t22PpZ/TieN0RIZD+3j+Ox/kTQG3ByGH9+KyR6gKA7/rD6zKmPLsQa+H6VAOpl8x3jlxYbXqzPE8NkfygNcutduYZOeO+wTFg5GeGYQw/sVjLMBLnmdYaV+sdr751qs74MkNPVEtiz59ky7E28KCWiUhp8cxI+zcDDU6qavb101qjGykxqJ0aKYmV8TBS4nyje+omaVMoqjsmQpSk85wcssd5tVNnewJDpTMMwJvtHgqFVKaF5qTrhHMnmfri+DYLm1vWWE/HmrzuurrK5NbOx8+sG2CIAXFgO/cF53d3PFMjJdFB5QHgeKVk9QuT48qTe0prCwHikvHQ+Hj7hHISQBKkuxepDBRb6ewcTCRnhPUnUsCTAuFbN1IoBFnUxGYfxMbMaDlZ77nRtZVQMNOjZRE1QgG61OJD0E6p1zEvIUSBlcTDMVYpZpJiM2Nl6xkkgirFkh9uYElEcVuOpi5g3xMXPisF0hvtXubau8RrF1fN3OdbHZuvZRmMPavUrsyPG1wvEfLJfa6FarMN0XdYpOOkzq5xext33waSnyVby2iB9+GtSKWtsnPl6LycaKFk4F3Ce+bEQk2s+BLaQPIBqbqc108tBc/0fpaQey/O125oq+WNsJ0Mdn8FwAUA4wCeC4LgXgC1Hdz/HWFKA1V0UV4/c0OOR1ISw8qAvFSMnLU3PbxnwJb9bWasnDhXPkY9viJEuI8N0rt07cp626uiJRlBh0Tt4KmDhq0YAD40bQLIH581/18/DwD46fC9Jv14nVfN339hAUAO2/jsvcCz323+/68eBT7z/6R/e/85YO8j13VseeYT7M7H1YW8bV23crRczJXPcrc9Ol/LnxfOxmeXjcPjG4zSy18ii5GMHLiG4MTm2dRI2ZsBmSqqPsEuBTa+iIsLq61c5mY32N/q4wxza3ZDMUvcyJF+6gmVDiCtUkvXVWu7SkHHa/ciKzimz8crpUzCVOs00eRe2Z4Aa5UrK9mKoOts0v1rMaZdSsTx4HKl0bV+J6ZiSvhJmsG0/u4aT9fhQmAzj1OVfTd7Ds+tNC3HXWKIdatGUtAM2A6v1B+sYWu6yhIuaSCjtUYrqRyZz9LgN/86J9Vgtt9iIbB6WlP2bfn5kOCfYU7C5eh8TWSwpfkqoV96kRKrazS3pEAgjLQYbLua0u53AJOMSE0nY8iDfgJyglnrLMRUa+DRg5PxnnWSIOKJjpaj80vned8eTmyVhVzPOIzN0v13WxYoaceTd6NCYmV2vJKB0kbKbg8ggrSC86wXWI+4igNIiWnZRXVIPf0UpGponFysZxicAQdpIbY+wGJ5B4zP5M4JQlZYfAkkI8g0bl2bGi0nZGfcLgjvGYIkc91hbr0+MHlivy865waYmIMnVehzmuPSc7ZrrHLNPDa3mu1IsBsEQQHAktb6kNb6bdrctUsAvm4n9n8nmdIajwYxs2tx+D7KYe1jXzGBwLVWLu9U+/SJqwAGa+xykxzR569sDqWZSxJFbr9SP5vbaONVnjBVstpWb6ggghs5mRIhCADgvb+V+92/0/0xKBSGqu5t1wgS/vanD8obvP1fAYeeTX//B58FPrR5XSDVg06XGF59YMzk/LjbSjFbFJMf7RUD4+wXJkZKIgmHVC0lR9W3v5zO0ReFQNDKx4eQNupGsqamZNTrxJlP84yCAx9WdXJOxMqucJNong5CW5QKBTEwT2VpbHIcIhBzk230+0P7hJ5dlUq7cGf61FIjcdr5CHqs4ivB59wKxYW4ImSTKGWDcgnu2Ivs6hI9B1w+jQJvWpsoCOD3ggIV+peqf48JwRtdb62Nc2j18Arw8UJgKnF07ehau2zM/NrSmPdYFdlsgAz0h2imzLpIP4sra7QZVXpGK3FlV7jOLvmUJPlkqlnp7+SEu3Oe7qP0zJ9faVqoA3on7Boriwm0noAKIHvhymZfiKfYf5tDkEVBCJdq0SygBQxawv3mYm1LDGzomkvrc6iyYzCVzhTmHyXBTrren11uJO9hgFVonWcWcAIdAI/GfZpGZzeLcpgcKVnvC9Iu59wMUZLcsOffYwfTbRKOBuZn0PXhENlTSw1bmqsP9JYg2VobyUUJRWEF92PlZByAHNyfjvVr+dEKhTSxxX0Vl6iQfCgKnvk97qdJa64VTyrayaI9E5XMfAmVtlp06DtLMTLIJQOk8dDzwndHawUlZPmx+gXpp5brQ/mzt4PtiPSQ1loFQfBDAH6XfaYBbM/LdiwIgm8C8PMAigA+orX+8E7s92aY1gyi9tS3Xvfj0UN6p03cnTJ62UqwJNf6wSbHq6XEqfYxch59oc+0aA0buF5YbeK1928vqFttdFAtFfIZo6fvNhDlXivDXvxnP/r72zrmduxPz6wAQE6gF9v3fuq6jsH3PhLM0qdymEhZeMCY89gb86wXKbHaIW9r5l7VExJPDq9vLys5ib6sxmGkRNKPPCMnwQfifTp2XAbJA/H9PXmX33WkYGvXALI4DReI0ToAACAASURBVKAiOPQhr+CyP9Pa4EpU5VV8AQODzIPPSnrHR+dr4vbUn10uFqxnYCSeA/yZJHgqDy46YTZIeXG+BqV1st5GTiWHPhspp0kB2ubh/ZOJDFmotOWky9XNPDbkrFPOHe5SoYDH75pKKsznV7IcDGOVohUAyNDOHL1eZ0wuXNu9Hi4BEml1UmJFus5KaytgPi9Uyw1ZV7oRVXse2jeJxc1UKqxfxTcIDKmT+QXWd+4WZK4SyLywps6OV0Qt3Yt9ODBOLNYtOR8ySrLdFSeP+OEIFSBVpJudsC/CSiIJWqplibDCSL73/Lx3Tzh91QKCgJKh/D1N+6ZnKNHGtY6vrXdLXm+6O6ZQaYwKwSeXOKO1jrdATY2ULEksuoZ14RmxrpWGxSZMHBL2PLXRGFJwL6mRaG3eyUGS7DLHd/tsaS2VKvrjMfJDav/oRSqZk9a1J8bxchHIkL2lEkeB8zkAcS6HSovtZpTQlp4lHqSHyn6mikGQEIHdKbaTMOZPBEHwT4IguCcIgl30c607DYKgCOCXALwVwOMA/ucgCB6/1v3eLPP053bMPnl8aSgt15ea/cXZlW3DfMmoJ1CSMMizk4v1obRvyVl56tBwld1SoYCup2SMa4u1rcHQ7CC4bjI9vna13sFouXhb0OT3hiBDI0daIklxjV6EPtVLwLCpSnAoydKXot/1pTnjW9klB/o+T3ks6seqCkFZv/EMIoYC0nP00RckCK/veVL1aRBZXC9UYvKEevoy/bHxS8WFGVPyzd2eHCApYaC1tirHZNNj5aRnmBtVq93rlQQ/7HMaD6/YSVq/eyeqlg61VAEKnWqnRJCUhelmg8TNds9CYyQVWnbNUhh8ul2ktfVckkSM6/DyOSedx1ZPWe8B0lXla1m9Y8uNELEMxXW0Xw6XVHEygGwjZpHlCVkiqQNMgoUYhnnluRuqTHDungMAnI/1j6VXWhAEYmKN5HjyTAoeIqVFVnh6BvcLCbbxSlFEX3T6kAXSsyShMIqFAA/vz64lNHfcRBkFUDxADpVGsxtZ5FhpMsRO6vBzytOldtFXrhYyJSr3TNg9rHwbUQdaGJPLIi09eyKxlWbSgGDrMgvepN73QsFOKFLwOGWxL9vztNHJapJHKsswnEFNxLfD5T/oJxXUi5T4vtBa50oF9atqX1htiZ/Tcy+N4cJKE9KjRGu8xNlDc9uVkyPzTYbfLraTwe7fB/CDAJ4D8Ffxzxd3YL+vBXBGa31Oa90F8B8B/K0d2O9NMYn57/oe79pIiu5k01pjfnNLFIgfxpKeMk89VMCImfsS2wCpaPow1WPALOZ5PYWDbG697R2A3CxTSuPKensoCOzNtBfna95auAl8eHowpJYIS3wZFC+stkQ4lGQUVPr27J6M+9H9+177EKEJRrInYi+5YFec4KCfzW+0sXey6lWpp+DTd/U4GcPoxqT1OOmjMvdRWpOaiU6k7UynzqZ9ggTrc/fVTeCKcoVASmpEDnSRjMiS3Gp1HkENYPdBlmPtXO7QR0pZZGgUnLuBwSD474ITSCfPCNtmcbNjIQronvL+YII/kmnh+L0ch5/fE1q7SSqG3hc8SJ2oGseT2lyoqsd77ui5onlA8FNe8SGILBmdI0+G7ZuqWufpXkNKKvH3lAQhBdJ5IAWb3VCJa4chdcx8nMyTRN6HWS+n/5f6LqXjKC2z6F5mVUbXIqUxNVIS1wGlgRGu1UrjJr3VnGQUH0MyF9j+kyqcq/nMtiGyMK69nk38aLS6kVVYoTHwNdaV2KoJVcA0scKfmS0v6H32MyVvI/T1u1D6orAN92td5nPigXDXDDch7hJr5bGK90MfncgpXNDz3RJ8tlN93pGFwK52k1GVXmJPLgSBKDtI65LUljO/0Ua1VBDnOK9I3ym2Y2ejtb5f+DmyA7s+BOAy+/1K/NltaTeysktZ8686cs0F9jvSyIGQ5HyGMcpQ5kHFpfzG0fkanrnH/7gUUPeDo7uH6YQR2r1o2xXPQiG44cmZYe1cXFGQZFduRZscKVmOSj8jEjOfIJPIxMJBlfjYxivF/rBvZhdXW94BIJA64f0IpPisSvtk/SrexKzqO61pCu8e92HADhNHSd5Zdhy+yBmCu7mwdH5ZCeLrJih5+8uMA8cjiKJLfETP7j1OUiCpzjnzqhcptLqReJ85tM7+TuzgO3+j+Zg4jToPjozM2COnj5UCPl6dODq/ab1LibwrhSOagIKz1tL94kmeyZGSVRWjMfJ7OjtWsQKYrgAJjhyyI62zVVFCatBnNA5eqVRaW8kA6d2ilMZ4pZg8NxTIjLLrQ1I87nm5pFYPCJVnOi9apzhbMQW+buWJronrIKfswdk1b/dERfy8n15tni4u3dtRp4IVKY12LxIrZbQfiRG3n7PfDVUy3/leT+dwFaR9r2l/rtTrTsgJKyB1yJmoSsyTFsoJWumaWkgEoaf8xIJN6kTBGX/+6R5USvZ2q6xKvSIEWZIWbq4UF7vPlCjl19UN+CWo8wmHoIrWBo6icoP7TqgyvlkeCSBtJs2ViZGS2EaUciZkk4rE8u3OZa0N27mEEuzmIAeAWJZS8CdFWHhsjU5k9YNzy6tI3862Y8FuEATfLv3sxK6Fz6wpGgTB9wVB8MUgCL549erVHTjk9bOLMQvbjbDnTplrca3B3J1qROzyxgevjaiIXvTS4pAXH4xWCkP1HZJjJPZE5fQSUzA/ktdzO8DOLDeGCshvhv3l+TUAwFse33+TR+JnR+c2LShXP6OAxyfIpJftEaGfx7UohtD5BpfdUPWVA3Ht2MJmrlySNFcpYPKVRyPykHt3+QWZiZPgQQpWKgR4RIBJSneAHDpfePeL85sDIc/Eqkm3XCKDKjrHI6hwXnXVHZ9U4QQYs7JwOsdydHnbcc+uW+kjltdyoZDpvXWrPUCWoVmqHJGMiNYaeyaqVotFnbFC0zfLpYIlRUP9uJy1PVTaqpTkaXHyiqUUNFMvIiUQiHmbKitA2jNIu6Y/lZ2qFU9C5EK4B1SjWp3Q4nfIY8KWiK/oWPQdDocmeKgbJBA6w50j1F8rQePDSIutBQTlzuifxuORAotysYByMcg8XwkJleDU0/yR3qlH52viukvroJQQm4gr0W5SLQ3qsufC7wfdCz4fTy81MgkKa8xaY3Fzy3pmad8HWWCWtEA4Pay8L5OSY5xrQWsDkebXolIqWNBjSk5wxAkxwRecgJSPk+4Nn4Oz41lIrRukSlJLkyMlB4GQRbusOmztNYGgkZ4ZN8Ec9mHaDiMlvjNonubJFe0TSFElFIs7Biv5Ef/7/JVN8ThU8JJqFqVCgCNCopZ4GIZBHt4OtpN16tewn68B8CEA79yB/V4BcA/7/W4A83wDrfWvaa2f1Vo/u3fvrS2xM1YtYSZo3JBjff6CCQTe+OC1yercqfbly+sAhtMclSxlyfOnar+81sYz9/jDiy+vkXapf6WViD6GYZp2zVe39WYZXfth+qVvpo0NocvajZR3D27ibHtkY+kl7yuVVSgMx5Q8US2JjkSenbuaz2YpGTkEvr2yVPn0qZBfXm95BcVA6hgdmvG7R+PV0kA5Bzo36S6Sw+pWUckJGim5VTVZl3c1hkK68GZ61u8WqhfTo2VRl/f8qjzvioUAkyMly9mVoIBJZZdtd/5qMxcmyStjvPpKgRqv5CiHtCWP/EmSPik4n0nwSx5I0z5oK7r2EywAiOIgnSwUSHRC5er3ZqvwFBDTZh0heVEpFezrkyONxH8/c5X6bgPrWlgEXhQQOb2PVAF0E6u9PommyIFak1FA4ZJG0vUSCXoilYH3m+/ElXqBl4PQB1ISZ3ZM1vOlJMYjB7LrYS8HxpwyYfefi8m9ZttNjJQszedkzgTps9DuRVY/sJRUonWFkwwqbTMF0zrCb4nb+wsYH2SE7YeOxxESI+UCxitFax2bGimhzZAWUi98L7L7akNl5In4M5Eyn1vDsljnaV3hY19rdkVIviT95cJ+L6/l98uGkRY/X6l3rfFa31FafN/RuiGRnJGMKL3H+BGnx8oiSoLOUQqsQ6Ws+0hG81jqjb+dbUfYmAFAa/0P+e9BEEwD+Pc7sOsvAHgoCIL7AcwBeC+A9+3Afm+KKaXx9uLnzC8j17dqRpmzPQPYP1+qtrhpXiKvunf2mvbT7pkFyndxSJmV/ftv6SV37xA9tPSC2w5BWTgAmn2r2Me+YvJevlXKm21hpLzZe08s1L2hRPTe9CG/omrTfZ7zohdqkaE3z7qRxhND9IlTZVJiy5SMHG7pBS4ZBRO5ElrM2l2F9aZfoH5l3QRvUm+hZMcXamLvILeGo4/KjdZzN8jnMincKKh1nSqKNQ/v8oM303ceFXp2J6qlOAi1xxtGOlMFoW24I0mJDjdQW2YwXKmPzw1SRUKcnEA2w4bcp7pJ+x7EhgzY81Fi3o4im/04CUAtqKdyql/Zufji/KZV+VlrEfkUl6iB5chSYOdKuPDzoraDRApHqI6lcG13TpkKoNsyk7I0Z+fzyaW6WNnt5vW+9tHFXaxtiW0NSfVWCKoJpi3BQkOlxfmePmvZg1Eg7D4L1Gfb6oTJtaVAWp7D6Xe1Bp44mK6lhAbgx6+UilagJz0LFap8Z5Im2USGCxnOsEh7PFeR0pkEhwaSSqJmbQ18PYiUtpAr64I81Km4h77gPLelQpDsk5jCi9b8LuKuoqBpnUPIFbFnjPYjFUaOztdEWT76vpSwNsiBzMfJmHYJFe5xQe+bTCkZJUGIFynZdGmtlVupBvyTz7eLXc+zaQF46Fp3orUOAfwQgI8DOA7gd7XWR691vzfLIq3RRfwCvOc11/VYnzy+jIf3T3j32r3U7E9OGYkKXyc7z6iqJvVFSEYLt0T4kmdn434gX8caMARTgJ+mq2tE7uJb/cuzYSrR27GLay1vYqObbaSF60tQNV4tYrOdD3XnlzatKsjXgm+bEKnkzFf3jr0wJ0Ok8myz1RWlc/LswkoTo+Wi9/OTOLDeLMj5Tq1rWmvvKjbdR0kPFMhex7FK0SIjkqyfM10tFVEtFTLX6dyKDCGlIMUdXyj072mdViJ49Zme315Oz24YKRE54krhABwWm65hFGBxB64QBHiKtd4kgYFDwiNJBvVz1KW+1TPLDcvZpaCQ24WVZkaOB0gTTOS4DyTo0XbvIfVI8jEeX6xZlW8KYPkat2eimpBSmePHlebYqTZawXZgTcs438+F1VZy3QBzDXnATsnYolPpc8dMn4s9tnHSIhQQQmOVYgJ353Z8oQYgGzTStnL/YSjqhlOyQPoO7V5iR+9FKhMga50GyFLwTqRDrknSPJTUouupdSqvw69j6AR/C5tZWSaXeTt5vp0561bd61uhdaykHYsd70WnN56ONyjRJM0HekYS5IDwPJo+d4FYjSXmCObNtbHdAJyMt8aESlu+nkiiJ7BdA+m6zHko6LLM5DDV9+s9nx0ri4Sm0nfo3b3Vi3JbZvJ6zBO9bbbm0v6Ultc7Gpevn3K72I5VdoMg+CjS+18E8BiY7u61mNb6DwD8wU7s62abUtossuPXF25NuPtcjdSXLekrkaAc/cwN4GjRlcgLJOv2yTbnGbE/5jnWktE7RGLIHGQU7N7KUJZIaURK461PHrjZQ0msX2wvOXb97NxKE88KqAMpeXU+CXiEnm5nc+pV77lejLAtYJJBEqQvzy6vt3HXjP+cC2MSGV8j4i5flvmj87Lz7JpSGqvNrndgT5VduY8+a71Q4+m7+1e8l+sdlHPGGiqVlTVByu7sXo+0Umvvq+U47PRXqvjTOkZD0FrHjjFtb0NfsyQrpprlkr30kwcKrO00+JCpusMvyfxG26pW0JrKA4MTi3Xsnki1zKl/jU/7iWoJ84y1mQJZ7iRXSoXkXgMssHaccqlCxB35S2st675uJu0E6YB2j1ctB5SumSsZc8TqmUwD/YBtY40vPi/eD1phUOj0O+kHdP94oHpFYOcFgIWNtlhZpevA30E6dhOV1mKvY15bUb/WiHKxIL6raF2RXrVSBZnW7xeubCbSK/waUSuBFLzPjFVEBEzevAdMy0Ba7TVj5ftwGYxp/vD56cLBqZJMsFYNbQJUR3O6Gym0uiGqJTMnrsSBEZ+jeyaq1tzX2rB0FwsBVHwNKIHHx3l8IVu5pKotXWNJ1ujYQi25XgCHLNsV6H2TVWv/nVA525h/eSLDTUgljNhEKgdgsSazLrsIBXe9kgoXRNxVEuZXL1IJqSbfF/ldyfHZH48t1ET/QikjcZTyPKRfondT2kee/q0QQERg0XqrBP/gdradLIn8DICfjX9+EsCbtNY/uoP7vyMsUvqGVFpPL5lK4NufPnjdj3W72vmVJt78sH/SIe+2kbPiK82yXLNfRj5GxDXDVNjOx+yfvuPiRsmSYSrJN9peiHUiXTjmzTCfJ5rITXxIpADjUEkwRsnoHvu0LFAG/+5Zv+TMYm1rqJ7dsSGYnskkpzfPCM6VR4Ll2vRY2YtEigImX8i2xMrbz04u1VEZkHwk507aJWczdZ0tSSaF+r/cpBox9bow3DwSFgqOm0IlYqRUTPoF+dHHKyUcdBIeFGza2p5KrAAXC4WM88arjr1IJZUwAJjfiJ1EFoTMjJWtXkYyPm+UhiXNRk6mK+/BkxR0/XgF3IdEpxCkgQgdGwDu2ZU+h5HSlqQWQUvdQNrtKXaPdWa5YSUMlDAewJbvirTTLxzv9xBbJ+jeTY3aSZetnkqcZG4uORztXWuNrZ5cjQqVzFXQSdat7N/Wm10ZjhzPhwNT2bWO0FI0v/iV2TVRETk46H5IY+hFSkx4S/BraX4QzJhfk/VWT+zrnXIqlC70GQDu2zNm9bjzSi+tMxyVQVV//pxFSlskRpSw7TDfhYJ03l4xO1bJvLs6od0y0BHWz70T1bgf2a7+8oDRZXWmhEbLkWMC7Or2asNGm9Da0It05v3t9uyS1rxUpT2z3BDnMRHY0XXly/PzVzZz2kWyzxzZ7HhFLMzQ+UuBcNgHKRQqLfJe0Nw4fIvLTg5rOyk99CcATgCYBDALoD9e6yVqF9da/sKM12BfvGjIlwb1iL1UjYI5H2jjICMdxqqnk0wkCQ8P0Q/74rwJ7Hwda77t1DZg2pRh3E5V+EbZn51ZAQC8aYiExc20dYF9sp81OqEoJyDZ2bjC5qOzS7D7SY+5Tw59a4j+8ivr7aFI0cJIeT87gJGdGSaBE0baS5OXXvKHPBEaja3QIlMZZJViAY0B5GS9PtC34ws1sZfXZfAlIwibe20p8HfvUV4luJ+ERqR1UgFz9+WSrFDwxNfcMNKZwN6twEiBWqlYwKOMJIjGzIONSGmLuI4cZ57A60V2sN0TYIxhlCXpMcdK7717b0gXnQdMhSCwAmsJTu6ORyLaiZzKNxXC7P7bYoLg4Ofu9lpyqaQMWZdAmERBhEsGVSoG1v0gyyPOo0BDQrvkQVKp4i21rcxttGVYaM6cBrIyRdyiHKZoUUs39ueOLdREtBY9h7xQJgXAkbLPjdZertogzYdjC7VM5RPISvpIjM0c1hsK/dhuYEnf43wPtF+eIAqVsp49gvlygijS8C05FWeehKWEFu+mchNLBC3nAWqyZrB9rzQ6ItyYHy8v4TcezxUpaVosBGJSmq6VJHk3M1ZGS0BL0XohoZZ6oRLfTTRPpARRwsItjPvCSlNsZZqLE4e+KgO3i+2k9NB7AHwewLcCeA+AvwyC4N07tf87xcrFwIJpXC/78zgQeM19L2vsSkYsvpKjNqyNJOyRfg6+VOEYZNthjD46X8NktTRUgExGjsO19jNfT/tCzDb+xBBVx5tp5PT4VKLpBT4MvFcSvJeMHAEfwibKTvtWdsmBz5PDkuyFuU0xiMuzyZGyRVgyyE4vZ6G2khGstN+2mmUqL661RCi4+L0YNpgnO0V7IWdLuhwzYxWRybu+1ROvNkEd3WpAAslzzpMYR9195bHMAmbO5X2eZXE1//K+t2PztUxP4GJty7rOUhXM9ClmGWH5WFzG0xSKyAKKyIaJpgy96TbNbmgFDgn7NTv+rvGK3TMaD587oKFzraTxnFisi5q+/PlYb3WtgIQCazeJxonwMsGuZhDy+HsnFutW4kHqX8ybC91QicEjfddFYdC5SwHlsfma+G6UekrJxqsl7BHev8RbIT3TYR+2+/nNLVnKJb7WlFziaIrp0bJY3aZt9k+lWuVzG63M9yOlrOBMukaSVNfMWNmaexzWnm5jV1plzevs9XUDy54YpEvPp/07rdccwUPr0+5xW/pLetZm2bvq7LItx0TBPYcsS5XdsUrR1t2V+uqFSjJgWn72sJYIboVAfj9KUkHpcYCHBDQTyW5JFddmNxT3lSTRhTm+nsPgD5glal0I0qlNb7J66/p+27GdhDF/AMBrtNbfobX+dgCvBfDPd3D/d4QFCLZVaRvWvnJlA0D/7OVL2b4Q67PuBAT2xblNFIL8nsC8Pk4JqpJnX7q8PlCGxj1OHgGIj51cNH2Ow/QI32g7vdRAtVQQCUZuRaMesTEPaDghDyQdPMmu1jt4eL8fPJoy6tOjg2HA5Ej7Qu4J6npEkPrgxvveZ8Yqw0H6IyVWGfOsWiqIcFbXajEZmE/fM2B0NXd7Qqnp+FL1iScGSK/UPZyGqZZISJ1Lay0RctmLtEhOlwe/pgDKZTanANuFPQPGOZOr0HWbACvuGzTHST/fnQO757mMBMrbh6BKCojbcW8h2XIC304/O3e1aUkw0bXhPZHnrjat5Epafberv48emEzmCfU/87nU6UXWtSJJLG7To+VERxpIE2Q8+F+ud6ykB/1ltGL3EPPA6Xw8r+gaEkESJyCcHatggwVN1KvJp0kenPP5K5u50HsgW6nqx5I8O1YWNejpPkmokUhp7BGeRTpfKWl7erkhBtW0NjWEyhvN4XHBr4qUFhOvEjGbNC4XaiyRrp2Orz9dNw3z3udVdQnWrrTNLp0EqOK9ZfNmxZYBS2GxgoyS9Xy6CIUss7eUTMmgFgQ0yHi15PS1Z49/IvZf7DUoe73dbfKQCGEOQZ/WGr1IixVSes9K75NIKTFJSM+J1AZ0drmZka7TOn2nSIiHSqmAiWopw5IOmHfMk4fyJbRu5ULHdmwng92C1nqZ/b66w/u/IyxSGmVEgPJ37rZjK42uBZl62Wz7Uqyx+5r7r012CDCLb16BR6pwpYyO/o/HzGg5N2jOK4p1I7XtyjW9AIYl77qRNrfRFjUXb1WjPiefflZ64Yx7wuyvNjpiICUZveTdvjvJKIiSKjCSUVDnariSSXP1zHJjqHaL5XrHC65NFiqFZzzg4Et1Ewz5JqFWm11vmP9WfB0fHdC6QA6LlMHv5cBpp0fLYpVeqq4CwKmYzyFb8TVjdKsKzUQOKTveUqFg6YDyMbnEUmKFVmuLaZWcW56EJPK1hEhGG6eU74cqeKVCgVXP2pZz2CFGVQbBHa8URaeTJwmmR8uYYAkqqoBngm8pALBIe+pWcCn1SCptQ69pGz7PJqslKyFBQZHLNGtVtcpEcGgCQrqnLoHPYyxoouvC4al0Xdxrtmu8ImpIh0mCw55TxDKd10soPSd5+wIMH4K0L7rnUtKnWiokME9utPY+IiQPCckxLTxvvUhuJ+hHUMUh9RmosRC00nlMj5UTzyJ0SKwWYhgq31foVEwTciY2XnrX8HlULgQJxwiQkhu22bsmmevs+te2wkwrACBrNvPtLqw27YBYqLRqp89eSqLRnK04Otv8mV0V2K/JJ3MTwScW62LlNKl0C/c9L9GitVFlyGtJAexrSTYzVrb8AVrnaE1xdakBc90l3pVIaSgNVIrS37Jkf3eC7WQw+kdBEHw8CILvDILgOwH8Pu4QBuWdtEhrvLX3CSDyI57Zjs3F2eCdgOjeqUaZQWmBGNZenNscimCHnDdfNlkA+PLlDe+Ag+wrlze2BWE23928pau6BM957f23D0z/ZMwi7JNAILhcXtDo2kip6N1rqrQhpvBhak971PxeFfTiHYRC4BYEGIrteb3VRX1A7yu3F+dqYqDmGjlwrkRHns2tt70r0u24Ul8u9R9IN4ExZ7c7sVDLDQ6katexWMLFNXK+3ERKAlHNyG7EJD9Cf3IQQEyquv2yAINJB7YTLAUB/DzHGBM9fRoEsGScaBc8qKkUC1aFlhKPrubkXdOsz1dwwLXW1nuCHGhelT7jVAkvrmYh4bvGK7bzrbOOsnLIp0LBkY+0xqwV7AiQ1BzCLDoWHZvreYZK2URAQkUyVAq7x7NwTq21mHhMqoXOvKVeTInVuBdpEUVwOafqBhg90XYvG2xfXMknaVQaePrubBKsn940rVPS/o4vyPDrRMplAKnYzJgNhZeSQxKp0FbPrqLS0Hjrk3IqpgRpdknWqqWCdW+DIMBTjJyNk1+Rke4tr9LPb7StNZ2SoIPaE4qFAFfrKTs6BbJZfej0XEgiy35uVAZ1Q1q8ZJTIrZazSSoXETk9WhZl4yjRJ7UbUZuA+76n40qtOHmkgrS/g8IaLCUEyBZqW2JyiJ6/loD8O5OgB14Odi0LguDBIAjeqLV+P4BfBfA0gFcA+AsAv3at+7/TTCmNCAVg/xPX7RjUr/uGB3Zft2Pc7vZXF9etPpBrscmR0lDONy34w5D4TI2Wh9a8nRwp51acB1mjE97STMwfe2EBgH/F8VawEae60s+oCnyPZ5UxVMqbnTjMgV1JtpYQXAwX7PoGx5HS0Bq5vaySlQsF3O8J7wYIAjf4fMnRlII6yaqlgve2xBEwiK7hwkoTCOTRTo+VsdbMrjOSJihgqhuSExZGygoCyahfLE8f13WatM6H97mVJEAO4JUHi7Er+wEY5/cRVv2jdc5qEQiAvVOcWVaAH+eQMrkyJtJ4eMAzUi4klSIgrcjyhKGGtgJrURvY0eKVql9u1TYZM/vs8lo7EyDwcxeDCOc8JWIwu5n8XgAAIABJREFUV56IrKe03EsbzzN3vtF53SO0EeUFjXRcF8VA49wvvE+pOiUyNSslroNScEp2MYaDSwnLyWpJhD4vxMUHqcc8AzVmlXVi7+fv/chBd2ht1i0etNL85Oe80bb7vCnY5u+XKMre20hpa21Jg/T0M0qe8QBxcqRkvY/oHIjYTEPjXLze8DlYDAKrSCPpJBtitfT45HtFbHF156lGKpnk2owAbXbvfS+Hb4GSNVK70XqrKyZZKciVvpNCn7NfrG31xPcwoVokFGGrE4otPN0+iK3R+B4N45/eDrYTld2fA1AHAK31f9Va/29a6x+Bqer+3A7s/46yQ53TKEIBdz973Y7x2dMm2H21oNH5shnTGK5ntp/1Io3Hh3DWz13N10TNs0jJmfN+dmmthfuGqLBx2+pFGcbN7dg2Y+2B9uIVw059O0lrPT+gt5sbVYb6Vf95lvzUUmNAQJpue3Kx3pcQivfTUrDky35MEjC+RgiLVs+/sntyqS7CEvOs0QnxwIAeYiB1GnwY2sNIobYV9k0w8OtIDs59A4L0QoDch0YpZKqlWpt+SXGMSonao26/K2DmUp6k08KmTfLDp44rjULW6UUZhy0IskzXJ5dseGDquKYXgZzwwAneBgV8VMlxYdQuuY3EfmxDlFXfINA8h4GlWUnjt2GUEAmIMsHkgG3c6m+i6RsfSsNca56AXa7b0FapsvjClY2MpJW7TR4z9dV6JzfpkZHF0kyuT0A6TI6UZJZmgqE7zyeNU4LyK6UxVimKa+PReRkpQQGWi6rRSCHwUrCrtBaft5E4CORwVurX5hqvbuKFKoAcRu5W7Ok+jjsM54A91xY2tlBngbiYyNDZYLcTRrY+r/CchSrLDaC0jXyga7lrvJLMMRpz1UE7WOsBVdLZNqE7/0muZyb1dVYbXesZJuZjfg3k50/mM1iudzKoHK37J3cXNrcsDW36Tqcnf0dr8+5xVRVoD0u1jgUfJ6Nr4a7bWsf6005LgEb/cfciFWtwv1zZde0+rfXz7oda6y8CuG8H9n9H2Tdv/I75z71vuG7H+OtLph9V0nx72Uwgt9Hq4RUChMnHXF/U6Gf6LwxKay/Zl+R4WqO+FYoMfbnHiBdyqYLjY8v1jlef480yIqC41UgUdJ/wfmqk5M06TJv59oRWSvmyNu4Rx6tF66Xfb1vqu/StYJLj7ctMTmzPD+/zbwMoFwPv60hEPlLFxTV6vnwQH80+1R8g25tMmp4SiQg3jXxSQd6Dyx2RPRNVERKXV4U7sVizHRkWDFYYjNH9ppsEoOpArW3Pu0hr1DthBokijWd6tIwVRjZDVTVOAJQh7okfMam3z63AioQ/Drusq0/rbuPCRHmgmASPSjnBtwS/tLdZd6pWGhorDbsKI7EJz29uWfPuDDG4Fu17xyvf7vyXrtfuiarV9099tXybYwu1jBNM5yqhm3qRraVLXz0vVPXIlJKDRkI1ZMiD+sCO3fvLbbJaSkjpyDQ7n3tiuTJeMaPgXbJmN5LJiIR+VVprZkYryf4jJ6lC94i/42g+kNGc5lU4utdcVmpypGRtIyU73LmntZmjXLuaHmk3QePeE+X0x+bBkWfHytkkViGw2hUAO+B/cW4TWuv0ukV2sgcwfAp8PibM1nv7MFtrjZMxn4HLbL7W7Fpzhcbcjwl5rFIUW8EowUvrJz//XqStoJXPvVIhwN7JbHI1TKrmxfg7qW2FkcU3QIcibWQp2XN2ueEDhLrtbCeC3X5e0MvRlmNP7oln0QNff92OMb/R9u7feynan581le+dgmlUigVbdmKAXVxteRMPASlLbGcIxtq1eBHeu42eZApAhoVN30j7ypVNPLhv4pbJPvoM48Jq0xt23XVeYAO3D5W3PNDF1ZY3ed1SLJPlW0mlfnTf7VOCD7/7GCnDfunLUk0OxSMemtZhpBAEflrWBDG8d7ffOMgJlnqu7DHoXD8jdKp+ZErL1+NojoTLzGhFDExCR4aHjwnIJpZojhLLa1JBdSCLfPzu/iMFsSK6i/XnHpuvJezGZhtyUlNH0ZDPpL8nDM7seETMRR/RusolWaYSAiZzv6jvkG8jSVS5gXzSj8s+c6vgm22zRtMzTkCAkK27iVa6wzjPoYlEMkVOstI6A72uFAtW+wSRDtHzoePjH2HBAEF2eYJm13hW/orumSSlcmKxLq7R1bgyKiXR8oLGtDpt74/ujdQ/f3o5H8WiNSxiPJfwR1qTlmpbIrMt9T5K3ANiBVFrBIFdsTzpkCBJREEqTnz32zchMfi7I9I2W7X0vc12z9o3PUMjrKeV2MG5nbvazFxjruHMj8evqdSb7SI2pATVnomK1eudPms22dZBR3PbPT4P3OmQ0zFpo3uPq6UCDgsoua2cKi1g1k3eE58mNcxYJJTPSqOTyy+htBaD534SR0fna6IkIQXpUptbtVwUyeZud9uJYPcLQRB8r/thEATfDeCvdmD/d5SNV0rAoWeB8rUTI0m22uhAaeBND++9Lvu/E4yyo295fP+O7K/di/DUIf8q6Hi15N1fCaTO1SMH/Blr6aW1nYCeFvBBzLE3y0hC5HZDLoSRFsXnJTsVk1n5VPPJ8aVs7SDrhkrUg5SMqiW+FXRycHzJzWjsvpB+6ovseCZiUpmYwfs/tlCD1n4w8422zTQ8cN/zBomQV7UlyPPZqw1xn1rnO0J5FVxXf5MsVMrqDSQ7uVQXA+20ciZX9NxrSwkMt7p/YaWZObfIYZKVtC93jVcsJnqqqvMpcGKhbjluxLLLnXeaw3R/E1QBW+cirS1iG5o/XNZrpZ6ds25wSYE0Oc3k7HP2X/o+QWJTcrfUCaYgnrah68CDNAqO6fip5jCHh9rQW5pvh2ZHk3vu3guSSOTf60XKYmw2x5f7HAFzzSVt6DDnuUyRGNnvjJSLIlSZqtFSgn+8UspNRNc7odgbSUG1SIS1FWb2p9kYJGK+SwIxm9t7C5jrzfu+petaLASW7Jok+0NJJotp26lwkw/En5nRcsFiQU9QOgxdJOkmVzKs1uY9xwNGWuf5GJqdEHzZV1qj5fTVSrJCStsEWWn/M9hnypEZyqIUSF7Rek6UFlFJSmcTTkCa8Ns/lf3OSkOG9vdy1lPAXCdJdmurF2XWGLLTy/m+wq6xijgGSmRIz4xS2qvt53aza2/KA/4RgP8WBMHfQRrcPgugAuBv78D+X7Yh7NMnjPqTpPf2shn7y1hj9+6Za9fYpQqPxGpH5kJbO2EkLo55Rox/gxxr3iNIWe5dY/5BNdliXM2ToJG3gv3FuVUAwLueuesmj2Q4m1tv4/WepHHU5zXjcf/IafdNThQKAR7f57c+pE6pX1RHL/JBjNM0U6kn2JvtWeUTgkhGQYdP5dhHr5keMQpMCOo4yAjJIfXB8+e6WirEWXX7+pFD1wuzqfi1Zlc8P6U0HhMq+L1Ii07+1Eg5CSS5XV4jWR/7O2nflxwE24zG5h4s122ZIrf/jpxSV9aHcw80YmeQV5zc5GEvrkw8tG8iWccibTuyVBxy+w/tfkAk507nNVYpolQIkjmrtYGO8stDARONkZ4j/oyGcT9p8nvCfO5ARNmzQden7PT1llhPMwUWtXYvQVgoJROBcUb2THVa6YyebBhl2YB7OX2OABH7ZNcaiXQMSCvNYq95pMSAg+ahTEKlRS1Rel/LgTjd5+z+KqWCxZ0ReHxHui6hA/MFzDziKIcrQkAaKfv696LstZcQFPObW9YaQXPXeh6UfQ2lxFMvB3HEkULUk9pgQduWwP1wYbWZJNYBoBUna3mFPoE/Oz3r/HyJpZubWyGmALJUCNBNerzNmmGxjef0pG+0uuLna438ZO1as2shUvi+AJlxecRJOJAlldhMH7lO5pwkaRhpjXt2ZQNamq8SF4fbenCn2DWfkdZ6SWv9BgD/AsCF+OdfaK1fr7VevNb933H25n8KfMOHrtvuP37UXPJvfur2Ie650UY9LZJW3rBGznqeTqgUoL44J0uC5NlCHHzmyblIbnyauc3vIc2z+RgG5WbwbxX70qUNAMBr7rt9ZIcAQzgiZW0lOzZfyziaeUYBkC9jchgpb/3k86vZqkT/fWerSv1sKa7SD4L3pvsfju2ZKkQ+Pb4nFuu5MHP324ubnaHG0Yv82LKVTitqQBZaKUHf8uaVS3hD5sIl+RilhAk5mlNOdZ8COpfxectJYNClLwQBnr7bDr4vrrYsJ5I7pWR5jM0ctRIqnVQpgkAOuN3eQkmf05UnISipKwfE5YuINKbdi5JzJWI5CiZ50Ey21Y2seUWkTBzaecWBjUq6nicW6lY6VWJazRJ6CeeuJYkXe548P7eZmTskYyKR55xdbojPSCJX4/wtrWZmv7Pa7Irzlo4vrX+dUIZE01glyUCaX7PjcmAtBdX9oM9aZytoy/UskZ87PykRwIMYHsgGQYDVONhqOwGidJ14RZrGa5FDZXqG8/WBXYkqSS7rIUGneJatgTNjFUvrnb7H1zjqR88wqLN5SsH/3on0HeImbghe7iYO3HYblyDOfMe0zkikaZQskRA71XJRLKg0E6mkzJ/Qi7SIoqLngrOXJ4mWGJEhvaO3epH4LF1YtbXLuZ1cqnu3Fd1OtmPhu9b6j7XW/yb++fRO7feOs8OvA+7/muu2e8rCzw4Bk32p2Z+cujqUdEk/I8dimEzYeKU41PZUuRsfgh2ZAvrtnCdlWIfpK76R9j/ihI4kW3ErWzdSeOawH9x9rFJEy7NHeyWu/PtOqQurLe+X2bBae0fnN+Ox+H2Psv++Pb5uZXCQEbzwbg/m9ZFyIdFvHmTkLObBkl17cX7TS+t3sbaVAyWWoaKUzLpbqDA3O2EutFSSozBVRLlCzKG06fbm3yN7bKeWqhBu35dx1B05IqTBCo0ZsEkAedAeBEGOPNBgCSM3eAuFQLbdi0TJHjdQdOWCAOA+B37MgyKp9/D0ciNBKgBALX4WIqc/mV9Hum+NrZD1GZatvjxJrujcSsM6T0qg0jZKaTQ6oZUcleDxe8YrFokVkCbbpOCmXCpY95eMKrTuO4YSJdI8XGl0RaI5uj4SjPPofC1TDQNYZVn4Tr/Wh0trLREVQUm7jtDrGDn6xYBZl9xtI6faS/OKn9fZqw2RiZgjXVz2cDr7hxgJoNSC4EpPUWLFgj9vZAnTsgmi7L4lOHKklKVfLSVgJqvZebLZ7jls5OZffo1PLDj9z/FF4D3iErHWC3ObGXk4ml93C5Bf+vasgMAKIyUzhGtq88gmeI/ObeYyJAOyn3lxtSUWVai/OxIKHkmCQIBsT1RLScX6TrI7r1b9EjatNU4u1W/ZXstbwbZ6EbT2Y1z1MaqChp6Q316k0OxGQ8kekTOx20OflWyxNhyxEDdiOvattt1om9/cGqrn+VYwclKl6odkJxbreNqTRIr2OYxUlMugm2dXGx1vyDAg9zX1M3oNz4z63U9yPHyTeSTh5JNcipTOVB7zjIIF35746dGyBdnLs41WNyN5AqTSGa7TTBl/6Tlfb/XEY4ZK1ow8tVzPEMYABqYqVc2SXtEcGLNL6OL2hAIm0ORjoTM/wPoEr6y3MrBmYEDVVgj4Wt3Q2g9VkfmpnVluJMlFINt7S8eSql0uARE/VWmb2fGKBQeloIxLqBSDwIJwk9NrVW21tlA/0rFGysWEBZYbHZ/OmTvNF1abmcSL0jajLZC2u0jBodYQ17FepMUKKQXGnICIj/Uu4Z1EQZmkX75rvCISStGaKUFJSRpQShRpDfE60iN7v0BYJyEsysUsXLUXuf3ryIxjtFxMyMXMNnIShcc3EoKB9Fz5enJsvmbth/pwedWY/srX+V6knFaAbJXbRy86rSTbsH2uXEH3miclad/8/IwmOdtGuAbdSGUQP3snqxliTvrdRbaY8eWTQ51eluUApXYFspkxmTyQ7kUeQMmVOAIYyaWA4ur1gTFHSltax3eKvRzs3kF2LmZClRyZl80Yvai+7pF9O7I/cr6PeGrgUuWin36qaxRQ+2qdAmkGdTtBIZ3TrUgAReRUf3OHyMVulFGPmC9jcrVUSBi1Bxn1LPnIFNFL3/dltlTbEllO8+yFuc1cuL1kYR/4n2RE9uLbi07PjE+PfKMTekPBCR7tmzR7ca6W0ciVrFgIRAbumqA/CzDH0nGmqX9fGl9eBXe8UrJkgMgurrTEanMzJ2DIq0KfWmpknH7tVmSFahK0Xf2l9Yk7youbW5YjSERw3Jk9v9K0AlmqUPLAYNd4xUoaEbqC7+fUUt06Vi5kms0lOq5bEeNBfV7Q7BLoAAOCb+pFdq41T+TQ/JgcKaOoQxSiLRQRWXDbSqmQyCORSYzgxP4rOeJhlJMoEaqd/PykNSRUWuQwSPuYZcdd6oEkHgwpsURoDSmRFQTAU4JkYb917MW5zYxeYaS01XOutcZyvWONJ0/zld9HKdiaHi1bAWoozBkKKHnlcdd42SIu1AJiQQMZlMfR+ZqFCJASu+5zrbXRDbeC67iayJ9Rd74lve+coE04P5dpOwmk2SQ9Nl/LVD3DSOOIQ87U7iMzlyRTc/pvJXI0YrR2nwsNjUhpUTmAOAjy2o+khDQF6ZJf0G++hjnvh9vdXg527yB77tRVAMDbnjpwk0dy69oXL8bkVELT/nbs3FU//UwyyrYdHCKQJLiTlFnMs/NxT8Yw2rxk/fo5hrVttAz3tT8/a8ipXukJB75VbCnOxrv6fXkWKj1QB5quLb2w+zEm07aUIZbgdsm27P+VUmEoFMLUSNmbFRpIdU19X650Hr7PQq+PI+zauatNb4QGYBxf32r6RLXoJU9mNF1lvVAgC8emyqP7mJHzuVuSqhAqTVqbH1eSSmtTueXzhb6ZSsE4+08cKfs8RstFh7U1v5eUj69QCCzH9WojG6i0eqFVyeHBJe1pZqwiEvAcnE6vaRhpC9FCAQcPSqdGy1hv9tg2Amz4ql0VpbFxSLJ7H6T9hFHONn1g1YTa4J+5erOh0tiLDez92X34jctvxR813oOzI38P7/zCt2O8PZ9s55JLuVrB3KTE6vzmVqaCpWECJMnyEiWAqeZJ1TBKgEnf6fSUWHGmx9wl79Fa7kulv4U55G4b8TWX/rZnoipClpPnnH2Ff3/RgZoD2TlDyXsXes8r/1JVlZKeIw5Bmc3yrTP7ducjYJICPGjsxt/jyXLq0a6UCsn5Hl+0K8k94XlsdSIR2s3v9cJGlkDPRXpIz9aeyWqmshuqLDkTJXzcxIhGei0k/09r4DEncNXQyTmQzBkfQSihaHR6bfKkh8SgNUeSSGuNE4tyzzxg3sk+6gW3m915Z/QStk8cWwIAb8bXl6JRJu5ayI14AEeLnW8VNKHgH0Ifdn6jjXIx8CYV4jZMNTg93pYICbsV7Pe+PAcAePPDO1OZ32nLC+4TdmzPSvu5q83ce+dOHSIjy6t28u2TXu4cfVh33y/O1bwYocmOL9ZE9tM8oyqK7zylDL0vPJ90gn3YpEfLRe8gerXRFbP5ZO7RetFgaBjNES1MIpe4h/ZPSBG3GtxPe5FYXt2xhkpZVZSE8ERDlKKgNcytHFBfn9srGQR28KSRlexJSZpsR5U7k+Sg8iRMpViwehLJMeVss2GkrJ49qSrmVr2lSnOkNJ6I53gQpFVnDhkfrdiBPTm4vMroEglFSVCCdBs9OCDuhmmPZoD0GedX3/RvF1Be+jIujLwPv7/2dnxh5AeSv3+iaHhE9tVewLs+8014KjiXYabVMI5/NrlhjuT235IKAU8MUOphdqwiIkYIpSJBjxc222IijZ5DKdm3WNuyeqDTMedDr5P+SHrW2FDmNtric0+JrEox+44OVbZauLi5Zd1neuT5/KTHh5OhZSH0cb8yZ+x27hslP3os4JYQAi6LswQPducj7cvuV4/5DJi/Mh2/Q3iyaWasbD8jCZN/IbnmZ6827AQRC8BpG3pueOLRJc2ihAh/JpTSuIetIRqmfcg9P3rnHBbIoZTWmB0ri/M1UyEN6DvmXxfGb+TlbNZnV7tcSqacW2lYCVL6DqEX+LpE36ZnRSqGVEtyRfp2t1s62A2C4FuDIDgaBIEKguDZmz2eW92OL5hs6TC9ey81++NYmkkiBxhkEtvdseSa+wWi5JxODNFLu9WLxJ6MfnZ6qY6D0yPeLLrcioVgxyuyO2Wnl0wl8MAt1k886DpTZnsY4jhJFkMygqyNVQfPQaoW+GwLAJPVkihKn2fVUsFbSxgw0NJ9k1XveUrPQb9Ak9tW7CgNet4ipdHuRd5Jq+V6ZyhprrmNNiql/udIQYqUbSfHxYWQUuXazfgT2VHecyzdU0myBMiXonDlYxLW5XgfLgzUddQJHsghkET0Uy2lDq9LuMOJbAL2GZ/TYpWUV9MAdMMsPPHkUt2q9lAgy51Mt7eSgqNDMzxQsSVvJJZytxeZKlR8WrnQb6og8s+OL9asPkNKMlCF+m8U/hrvq30Ev3Thbdj/H9+aHl/vQe/J9+I7D/8PfLD0I7hv67dw4cA3AgA+Wv1nGA83bNmj+BhuT6PUQ23Ow3wuSQ/1IiUyIdPckSpYo+Wi2I5Az0Ce3yMliefjdVAKHqjqLAUwADIa5VrrZB5KvBqR0pnnp9ULLRIgqWJJ/+PElHn94vy9wrcJgiBpN9jFxqZ09lk3Abj9vADZZyhA9j7bY0Lme5FSmdYtpYDHDk5mvscTELPjNhpDqlIHMEEbD9w6vchKJtAc4Ik5i/gu/mxypJQhKeyXODSJJhk+HwmEfGZ/MnsyHacj8Cycj1sUJVWBclEmgaP9SDD+UClLT5ybBvDowTuP9+eWDnYBvAjgWwA8d7MHcqtbGCmst3p444MvV3X72Wa7h2Ih2Ba8VzJaiH2Zi8+vmAyjpImWZwGCXEmUPCsVCt4kRK4t17ZuSZiw1hpzG+2hKoe3ipFjcdfM4CCdHJhhSCImqiUvqC4FaL7zVWk9FKO30hjq/oSRThx4HztF8KsBgSOZ1qY3juuJSkYO7KDAnv5aLgYZOZE8o+Cj1kd2iiCStG/XCAbpBvkSgymQsly7STga/71CZf/iaksMji/msHdLASUARPF5ZNmbnYpTnMzhVVuCVfJewo1mT5REcUmrLHkSQcLmynrbYhenag/f92S1ZFU1OgLT74XVpsg0O8orS5Ht6EoVa6VtsiMKrng1emqklPQyAsaJN+eefIQ9E1WbjCqq422Fz+Gur/wifui5Z/EblZ/B95c+hqo2x/pw7714sPs7+OrOL6D3zl8CgiCGagb4zFM/hYXdrwcAfGDzg+BG5+nOHbqO7txMElPCu/byektOoCRzR4aFSsnCvGCEAqN+iVFpHczrw6f9cd4FmoIEM5Wek+X6Fgs+zWeVYgEPsHd6UrEUgk1+r6+st63yJC1XbsKGGz2nvPIt9Sp3QmUFemnyJd32zHIjs0bObbRFSS8Xju+uUVmoMbGjs/NT2kpw0LrGd6W0ff5aa9Q7oVURltZJV2rJfDfLq0FoPClwfWFuE9kmkrT/eCvMIhFOOfeHjJI29wsoGloj9wnJHq2BVwjEijQnpWepF8ryVICJJXyTybeT3dJnpLU+rrU+ebPHcTvY5y+YXtSXyan624XVFr7+0Z2DwLa6kcUWKJnFjBj/4kOYQ7aw2RYXrMxx2P9PLNbwysOz3sfgdrXRychL3ApGL/Fn77299HUBJD0yPsRklKX1vQe9SHknb6g640vw1OxGQ5FVmKqXx1zVtP8Qz9zjn1ih6p0veuX5K5teLQOEuJDgukC2cv+Vy5veUGpKMEgVLiCtKlDFjB+LVy6BbLWUgqVsVU2u+NJ+JEmpPKR3EMgMtGdjvoKMVqrgWGqY+cyrIOQUW/IjSYUr3a7eCdFg8FWCxfJ5eWW9bVVpCErNbxuHHANpMM6vkQZsNYN4nnJpkWIQWDqpaWAdJFWvxc2tVA9VK9QbDUyghXJnDdO1U/jawpfwNYUX8KoDaQBC16NatgNpPm/o2vIKVSHcwtePngH+w7vxX/GP8Qftv4tfrvwC9v/VzyTb/ID6p/ipJz+GKz+8gF+J3pkkQWg9oh7n2fEKPv2afwsAeKZwFrNbl5N95CVWaC7NOLq0m+38BFIYabEalRKuZdeQeicUA+ST8drqjqsnVNPT45vj5GmaSlJl/TTEL8aVtzyI84ZTDVbaTkSJwVgMGbbWg8AOVCX5rMmRktUjLJHYnVluiAHvw6zaLiE0JkdKIhEZ75ennvqiczy3t8OcX3q9pKDShSNTlRpWostlgzbnNVFN762UmDuznCXMk+S2EjksYYHcPSGjkuiaS8RRUyNl8drnPV9APnsy9ZiLcPxQ5k4AYoklIbHZU8okD+5AgqqX8a53iP3el0wv4zuevusmj+TWNWLy3amqLmCCmH61IHfJoBdzPzIh12pbYf+Kk7AuVUt+hDiS9SKNBzzZpW+k/cHzCwCAr3/s1uzX7Wc05yS5DdcI3ukrIfbCnEz2IlmvDyQrbxwkfTXIlDK6flJlksx1DM5dbYrsk3l2bL42FMP41Gi57/NJRsGcLzJ5ZqwsQsokI6mQQdJD5CRqYcR5UFH63K1cU1bfvRcEec+DaEqQtyAI8KSQRKV+azegdpmHAwRJMoFD8/NgkmZ89rGo2hnABD1ANnFkVUkDE1DwKmkA4HF2HpJz6UKmpTEWgsAi8qLAutSt4+DCc/jnpT/Ed5f+EOFSEfhQhM8BpqwwAuC/mO98E03hCwA+BPwZgBeCI/gnwfeh3K0n2aBQaVQLClg9i1dGz6OglnGouIi7P/v/4Z0Lx/F9I18BWjA/AB4KgA09jk+pV+G13/Uv8ftnQ3z4U5cwUi7gfdUU9aU0BS5B8jtgnpdGJ8SP9r4HHy5/BB/c+mkA35Z7LYB0rrkSJ9RfK73rioXAgrCSUZuK+44miH9dQEdMjcZEP87zSMgmScaoXyV2tdkVA45ulB/0UEAhJcAwPB/2AAAgAElEQVQqxUIGHRMqOynI9ZPJpMArQPqMBkihqm5llQdZ7W5W0mdypJTpmQ4CG2VB1220Ukyuv1I2uzUlLXk1lBIJlVIhSeCMVYoZZEurE1pQ4wS94vTQSwR2B1myRym7vUNKDErB7nillEnihc7xgDQBJ7Fzh5HCvcKa2ROQJcnflIyWSqXQ/FA0NMxLay08KGhcEzeDxAGxe6KSvG+4rTUGE1jernbTg90gCD4JQKIP/oDW+r977uP7AHwfABw+fHgHR3f72PNXNgEAT3nqRL4UzUBOgLfsoGzN5EjJK4AhowqtxJKaZ7V2b2hYcajUtuZCQqDlQehzo+0zMdv4Gx7Yc5NHMrx96dIGZsbKXr2p5Fj4JmVmx8q4sOIXkOZV4ySrx/I6EnRKskYM1ewO8aKcHClhZojEz0S1ZDmEg+zUUh0PebQA9JM8kezEYh1vf/qg17YEYXWZOV2jTLwEuY6irFMLpON2EwAUYLsBOfVTuwGA1jo3ox8JDKW0jyDIrhVEAsgrlFS1fmgAS2wagNrH43OEAtlp5zOOaqLgzYUN7+4tAGdX8CZ8CSXdxYHiEkaPLuOh/5+9746zpKry/9YLHWemp7sn58wweWCQLCAoSBAEMaDuTzCDrrKuwq665t1FdBUUAyq6rphAQQlKDgJDGGByzjPd09M9Pd39Or9Q9fuj3qlX79a595160z0d6O/nM595/V7VrVtVt27dc873fM+hNlweOYyIE8PJraOBAz0oRw96nJJgH5WI1NRIC54vuQH4o/v3qdnXQQwZYNHluHNbGTpTFjozUXz8vIWoP3IUP1ufxozoUZw8LoXzo+uAI9uw1NqNR0pvBu66GXMBvLW0HKVIo3RnCvgB8D23URdb6ZwsbHVmYOe4t+Adl12JZb/qREfSjc68PnEu0nv3edfVfwsztpN3fwg0vv6YORf/Hf85pvfuBDLumMlRSHmjUifswzmn0rbNjnPKrVcNZBo/C5hFfTrjsPV3ydiurgzOL5QOwfWhsb03j0lAOELUVMZpRTmpXBTNcfKjjIBrpEYUJwuAPGqz6uB2nKCgW2PWWPO/KzJOvjASRdD97WVsB9OrfWJpjtu+WtIKUJ1BhdXB6TA1lSVeKTPHyXfeOnCjtP5rSTZZbWWpl6Na19qdF2XnjL661m5P2Zj6qPa7UcOA8acKOQ6luSlMFc9BzEftOQcHRfJ5leRgvXEgZ+xyKwTS4+HWBBnbQTNjuNKxuXSbjO3wRnr22ukELIcyBtzYdRzngj5o404AdwLAqlWrZKu+YYatDe2h6lu+EfHUNlecyq/aeaxYe6A1VE5jMh0UaiiE5s4kSznRwXEcHOlIFqXETPQ8zus+0Fh/sA0VJdFBaYgXgisIIrsfnMfVhI11bazYCwfy3Etq8lLekZjynDXS5wn7ArgLE6kxDbgltcI4caT1ik2URw4lsUheHUkTSGG2kKOD+sCN731MfilgLi8B5KKv3vbZiWRGbf4CiCK+HOVbV3dRl9tFeZB+6i+3cObO11OJVQ6nGq353zmY4BzFKUfXAd99F562E6gqcxfL+A5w6aj5eG/pbsStDLAFwBbgJ9TtKIBH7sY5AM6h7u4H8Itv4SUAiAF7IhNRfe8CnJ0agy/HbOx2pmNKz0qgsRR3d9+AGSW5Uj1bFn4aH1k7D3UYh8tXTMVt716JX/zn42jtTaE3Y+MDK87F1j3NeHDtBsRg4bJJU3D+e1bgzP96AgvaV2OOdQg3LkvicJeFp3clkEIM06vLcMnJ8/GN1d1YlxiNOmc8/nzTlXhwQyO+9fAWAMB5o8fjHbPfBAePwHaC1NZUJp8OmnGCESz/NbURwe8z5+O90SeA7Y8AGOM54dTdDrbw4zuVCd5zwpF2PnqazvDvR5PB4Y5PvpYvAFZNXmdUA0BlSRQxxnimPnA5lSlN+ReA6Nf5vzW29+apI9OYVh0JfgOVcw5FIxYythN41vztkHPIrxehGq0283wmM0FKebCmczDXOMU45tTjUfTXnx+dtm2UIomS9r2w7DKv7/73IWfsxiJWXl1hnZYAEMxb9j8DxEihGuoEiv5y4/XA0S7MZlhw9G7g5vxN9QlW0JSuCVeWsip7nTjnjGWBZd5490HjqNTN6e4+Q2+NVQgDbuyO4NhBL5tCdTnf6KCIQ18actUVJaGUiym6LAUtWrncDx1o4g+jFkugup3FlDnqT5B3ejAKZ0lwOCEv59STfVHOrJHd81GKqI4J27yIRmGjrisbkZSKQdGiQC05U2gfqYEJuM4iVQ3VhIztYNnUwmNm31FSu5S16ziOmH5NV2NyAQVx6gPXhVJGuAnI5ebq6M3VykJfV3KFxg+XS7a7qTNQfxcAthxqz1NSVo/B5dFx9GmuDXWRGI1YgG3jXKzBZfEnMBrdGHX7Z3BNKoXrylrdjfa7/1UBeNE+EQBw8gmzYNVvwxr7BIyyulBRNQFz3/pRvOfeRiRSEaQRxZ8/+1Y8vO4gfvzUdkRh45oTY/jwnDb87vHVmGPvw1yrHrHmrZjb2YS5tGLaDeBHAPHIfpt+C87+l7uxbV8L6tauDZy/Th3aM0osC0/ZK/EUVuLjF5+PtduP4Bvb1gEAPnbiHFxy7ol44pWnsNdx32GjyvMdRLPHBRfc6pho9ZUAUsseedfdN/p+lr7INXZf+TmAf8ktxqvyF+OjSnl2Eym/c+hOZVi1eZ3RSIYIl2u+oa6NzSPNKbfLjWrAjW5xQlj0rHHtbWtoZ59b0l3gFOpVHY5qJFDbugFW8wyMQScSqMQsXwCDG0M551C+Y0PnHKIqTP5yVYD5+RxdFsORzlxta50yurGfPvVnC5Yv+psbx3PtfXio7CbgV8A7AbyptBZP2CchM/oMoPdkt9+Z4HyXtvPrA9PxyTlDiEWsvHcN5USr10ANhnRl11NcBHd0WZxVNaa2uDm/uiLOaiCo6vZ5v2UcrXCa4/AGLRncnJG+oa6NXUvRu3UoBhQKYVAbu5ZlvRPADwCMB/CQZVlrHce5cIC7Nehw32tuvu7bl3Bs8BEQ1h9sQ7WQSirFkY7eUAZYmFxdILcI1eXYcaAi6AtCKjgDbk1fgKe+FAMu/7AY/G2Dm697xYqpfdLe8UY647D5kBz2Zp0y0vJAadvBSQUUkMmeII+yyZlB29JicaKwTFcuaig7T/I8hylVZFnhRPh0UUkVupqxHGzbQSoTLCeiIncdqTSK+X5SlLQkGgGUoDEZGup925MdK4E8M010g661uqCiqNV0ZYw6cFASi3hReyAX2auq4IVWXCOuMCWSoBpPE8eUAh1NWHzw9/hKbB3OiGzCCS8eBF4EfmDBjcYCsCtPREePhQe6l6ERYzFjwQpcdskVWHnHLrRkDanNV1+IB16rw5fv3wgA+PAJs/HlZYuw4d6/o8txL3L5uFloLwf2Oq4jqK56NnDWIvzX44uQyDp89nz1Yvzqhb24/YEXcULkAN43pxvvWD4V3310O37Q/mYAFl5Qzk1iTJCIkx9jyvLfEVxuuOqs4Ma4eq3n+yjA3H0D3JSF3UfcVIc9Tpam33Ywbx+13bTtsFRlj5aqOPnIgTuLceBSKT8VRCFWyx4BQG1lCXZnaa9+HGrrzuu3Hy1dKdax5cBBfWs3xjHnQ33QRQw5tXUy0jnH2KTsuLcA/HfsTrw39jTwFICngPXZaehw1zjggctQm3ybZyAmupNA20FUoQMdiKMEacQa1mJc8wFcEFmHaqsd5x08Ajw5F9d016EhmkYGEYzfsBlpJ4qPR7djmzMdR9pnB/rE6TOo76Ej7cFIq/8S0/X2M8uSaZtdw3hjyXHwUPymXLuROKbazfin6GNA/WPAf30N/xOdjudjcwEAc1ffh9JoBLfEDsOCg+quOHD/73HF/hYsjbnj9/T9tcD95bg15oqsRSIWcP+DOL2xHbfGWvGCswTRyPty/dYwa+iv0cpzSeJQnBObEwWjfVTjnB5vWnupz7sDBz2pjNYATWZs1kA+kA2CqewewH0mOQHMXA74oNYuLgqD2th1HOc+APcNdD8GO17Y1QwAOK8PVYaHGxzHQVt3CmfM7YPSTL6Jvb0nHRB6MOH1/a2BAvMmUB6H1FACcvkiYQxkAr2c1Yl9oPH8TneMn39i3+Vb9we4JaTjOGjuTIprMdMLcgIjiAHkR18AV4jizLn6l5N/+5TtsDUsvW390TgDdZCDp1YqfFGSCEaYUkWZjCOKShP2NXeJ1Lspl1lSkonyv0wiHv7ruLNRlied8iurqsauYmhQ83QtVINCt9jq8uaq3PeWZbGGjPfJAeYxqR/pjM3OZSQy5AdFMrgFlnpZrsv8CfjOVTgTwJnZ25GyShCf+2b8ckc5fpc8C9ud6djxibfjr6v34esPbgYAfGL8XFxWOxfALq8tNZUjrRhLFSXBPMtJVfnPR0ks4t3PoxiD1fZiTB49Fe84ZQX+8tRTIHUodTHKLU7V/HTOCFLHCecgUB+xXISY+U7TLif2lJd7iQgay2ZjQvMOjBrTga3tvDHAlZUBcpFQv3K6ZeWcaKMYRfWaihK2zmjGc6QFx1vadthcXgJX7u1wosf3LOT6Brjqu80+dWE6s960nuKZtm0smhycx7w5kZmz/LdjWWQPAGDr0s9jTm0ZnnviAZwbWYeJOAK8+kt8DL/Ex8qATqcUlS/3Ai8Dr/of+Z8D5wI4l77rAPAs8EEAoCG32v3v3+jvDICvfhIXTrgS5TEgAhsXNdQAz83EVHs82lCCDpQHHGlcSTzKJY04GVSiGynEUFMRwx7fNn4thzlWParQiQvX/xTY1IzXnY3eb/Z/tOKv6+pw4x/WYYZ1GN+a8gLObr4HFU4nzo6uBwDUNrjP6NnRbB3sVBTYvRVzu1OYFHXHdU1jCdASxelRl2EQAYA9uzGpN42rY4dwNZ7F747WAFjmzoGZoEAW4N5bVZuALklDood9P+4/6hquaj1gIKuezIwhWjv411601daGdlaklJyX/meZ+lYed58v/5xDv2Vsh9WyoPdAmNSioYJBbeyOQIbVu5sxqjQ26KingwlUKD5svVo/1OnJewEL6aaAK9pQqJanH0TbkZY5AXIvFTVKI8GWQ250Q2doDRQe23IYlsWLnQx20CJpgiBqCAAb612qu+R5pihJKxMh4tCdzASMCx32ZCM8Up8JeaalFCgS1QiTj17f1mNUe+bQ2VtY0IoMgULMC8dxPMEp0wLbD1oImup9OsgtjLmzI2NcZaXQXKIKl+xqciNd6r0girnal4wm8gi4RjhLrbOdPMcGOVVGlcXQoAToKLdvFmOsRCMRwHFwdfRp3Bq/0zP0d068CFfuuwoJVOAbVyzFB0+bif/5yiNod7L0+mgkj76qCqNxhqxqLPF5q/n7cLTVucx7RI2gcIwFlTrLRVDUw3H3RO03Z2yq90xthwxt/7fqeNkx8RJM2PdDfKTnLqzBte45KOe5sb6NdUDpFME9RwwzEaUydl4EWt1HR0mOMU4qep64PMeKkmieoq8fOqVoMoYmKEwXB8gyPYJ9o/c39dvveKRxFrd7sSiyD49lTsLYk69HavIYXPd3l4a/eFIlHro4idcfuxvrD3WiFyU4Z9E0nFBt4c4XDqLFHoUUovjShbPxbNcs3PbMAaQQw1tPPxmfvuwMXHLbs9jW0IoobPzt06ejp7sHn/vFw5hvHcTtJXcAAE5q/DNOirliZ5GdDrAzq7VGp/kNYHmsAj+LL8Raex6c5JuBhjIsa/477iv5HRZbexB7wgKetPCSncrt903gVAB7y4Afpy/Dk9GPAo6DT796EW4ubXG3OeL+dwi12J2ZhBsyN2Kt7x7vdybi4amfwdmf/jk+8dPVeHmPW2LzyU+dg65kBpf+4Dn3HGaMxZ+vPxPfvX8j/u/FfQCAn79nFS5YNBFn3fyQd88333gR7lu9F00PfgP/Er8X76v7JpD6tNuVrIOjW3GCbGecd0DuvaWWlQJyzyO3Xtna0M5Wu8iVw2KcQJUleeXOCBRo4ZxmaU2KC0DpAvo89xEa8wgGHagIOJdTNYIcSK36vBP6LvrdnM1jKSQ45V/LJ7pTOGmmvP4tGRBc+YTAcbKb7M+K2UhLo/hBixYuZ2mg0JPKIGM7oeqxDiZQ7ppUmIwcG5JcVlpMSZ//bQ3t4vI69DKcIqS0E2VdRtV30JUtiTFeqExOhj1HF+RABtyJTMRFRdp2UB7Xi5/5HyW6n1IjnRxIuog0GbBUR5ozbrT5hZocXGIRqOUydFRULofMbd9xc8IYo2xTfYJ1imVsJyCWSHOTOqajyGDa6q8gsvlXuNU/bD7+LJ7aMRqJfVvY/nJ5cuoxOeVStZ2ogP7LgRsnY8rieeNEXYCq1G7dsVSHBtcd9fjkgCFw6ToSA1nt38bp78WZ+36Ii5KPYTTeg3ZUBHIHa0eVoikRXIjvzjpoVOcBjbXuJEf7tdlFeK62Ml8z9JwF4wPfe4Yms09Gk5dLv3EGsvfs+MYMfdpU38ayr6jcIUe/pvsxIeXSbDtQjsnKc97YmQYWvA1/3zkdPz2wGwAwefFKnLB8Cr79j4e9Pn3pzZeg8dWDeNVx+/Dm0nGAZcGxIkgjhjSAmuoaHHC6scWZiS3OTPTMuRJ3XjUbtz+2Bbe9eBQZRPGLK6fi/NEHcMefHkV7dxIzrMO4ZoEDu34j3pp+DW+NvgYc/CPwEzevFtlL21a1CFWT5uCVbfuwNTURDajF55f1ILP9UUTT3fhk7AF8sukB4GsAuZs+k7weHzxnCVadfzUu/s+n0dKTYkU1udxudz7KGaX+SLx3fVVWi49VcnvmSky0WvD+2BPAXz8N4GOesTmtOv8+jiqNsbostP3CScH3C91vbt6uLImyzunc3MyPVy61jBxKunJFsYgVmAccx02/45aHpEIumQOHGkaM3SGOB7O5jJctH6mva8LfNrrX6aQZckOzEKjkgonyoU409W09WB4ilEWTJpff5B0DwckMAFuOoRA21ScwOkQppeOBx7ccBgBcMATr6wI50QepMNqR9iRb048DlYqSDqmq8rgnPFUIJGYlZYzszkYTuVIfBP9ITXmebBllniLki6fIaM/kjFINAbZtQ06UCtuLUsqYE5VZw7OQVgA9d3lUYh/tzF+Cg7BbE8FNa9SliVqqOsLIo68++0c10Q7ANai4a5tWShX5D+Xv5/XR+/GF+B8Bl4WMw85YXN77DXSWTcSGycuBHbvZ/QBNtNOjeLv/S4w57o6owjadDPVabZuL6gXy9ZhjqQt5ng5cWMBLzTVXDUyubW68B/ocrwCWXAVs/BOuj/0Ft6TfFxw7GRvzGQV2WuirDhEam1x0fPOhBLuop3HG0T9rKkvZ8bnbK7MW3KelK6Vd0Otqhe9rdp81zhgZW1HC0sLp1s1kGA092ZShUxKPAwAeyJyOzyt94uY6NZWBu17c86E6f6IRC6isRWdJLTJwgwFdpROAE1fgt7FK1GVcR/s1/3QJNuxvwTU/egrLrN04b84ofHJlKe7dlMB3tlSjAbX4yQUn46Ilk3D9tx5HU3cv4lELn3/3xVizuxmfuvNv+ELsD7g69ixQMxfdbU04q/MWNKMKF087GYiV5PdJAUe3VW8BV2FDvQYqq+/r6Q+6xm6XmyJF90oVDkvbNlvpJFfjnFc7BnjnjAPgRMZAprlcR5MPtmV5kV1uvG5taGfnHGLZcPV3yTkjLXs4lDD8zugNhr+87opTXbpcVvPxjYq6bIS0SqNoV1yb7sQgXazT5BKGikv5b9J8TyBXk21MSDEswJ3ouYXDQOKeNa5AypUnTRvgnhQHyvGUqg43tvd4isyFQAusQkq/hPUHWwOeax2oDqdOBVIFvW5rK2WGOnnapWUOchEh2bXpSQZru+qwqT7Bvvw50PGl9zOZsVlDldtOl66Qtvm8SN28oKOj1bW486B6zWnhpqo9E+159rjgmLEdsCWv2rpSLD3Yf9wTO19xDV0A6ZoFsL/YiFN7f4QG1LLK81572f+4axFcKDNRU+XvVkbFfK6Sh8wZE6pBzC3S1agxN7xUI5ETSFPHDhmP/kusjhtuaJIeA4FTcFdVlbuSGeCS7wIAPhR9BEDQ2MtoxmaOxqwaD/oIVm1lCWuo0bXjWCC247DGDuXfczXt61u7WScGGQ/ctaHz4ObDjO1gIZez61G2g+c0q7YCSHbhgpY/AACet5cExhF3XdUqC9zY4+x41UnBXWdVvd2PHpTiZedEbKg4BVh1LTbVXIAG1LL99I/1JlTj8+lP4HOLngX++TXctuoxNMNlIunmCT8kzzo3D6pRYvVYvSjB4dFLgF1PIOrknmeuzi53jT21Y+Zh297YzvYByCpaM+OB0gM4TY3N9Qm+hFc2FSjFMP/GlMVYRxlFpLk0Nxrj0nf4UMKIsTvEsWafm/ugKjiOIAfHcbC7qRNvml1YqCYMqE4eR6njQGV9wigyh6mL6u2j8ahL8Pr+ViwSRs6OF17Y5Sb2SOm0gw17skqhUpGxaMQS51t7ThzhmBoTIrK783AHKkuiYvXyAy3diEUssVf4cNaLLKXb02JdmndPgk+S/oytiIsj2FTCTHqem+sTIsN4c31Cey0ytsMuTjOOwzo6KM9MF81TjSNdJDjt0eT4/C5uEZjoSbNCVEDO2Pzg4W8DAK5J/jtar3sOiObmUE4kjIwGtR0/1L6o9TKBYASEExVSz5VzQEhqq6sluLjyPJJoqzomWMO6AGUZCEYXuTlGNRpmj6sAyqvRZo1GuZVEBHZgob6jsYPtE0WJ1GtOBiW3T9rmVetzNUO5/EN+HObGdPC3UaUx9v1I4lgcNZWeBU7dNpWx2XziI+3JbB+Cz09pphP4TzdIccAej16UBBkMzH1UnR9SNoDaNheJrlSUl6fXBN+5nEaJejROAZuYNnl9Uu4N52iXGOXc/VeDCtw2yZj7Lrmw7R7vO/V67mzsYEUXibHFUdRJZEodKw5cbQGuvbSBdl9dWeJV2Qg0CGA648BO2w47T+lSWYDcuYTVxRgKGDF2hzCIYnbmvD5QGB7GOJTNseurcjoEKnbOFa3nQHka0igcABzORrPC0ErWH3TrThZTYkm3cBgo9KQySGWcUIq9gw30EpsovO8b6trEgmS0oBsl3P5QW4/YmRGNWGz0Q4fedEZb0oQDef8lUU8ASKb1tDEOlGYgMTQ31SfEDgZaCEwSOqBGl8XQzuSdqTBRzLc2JFgDJqOplzomOx7UOYDuj2pQ1mnKXphKBvWmbDY6Z1lBdWjChNFlQNdRjM00o8Mpwwv2EpHBp0YajjA5euqic0pVcFyp6QGs4RhR/w5uo46pHkZBWL3GXIkSiYKzZJsAPZstV5T/nTrHcPl9ZPj/vcSt+Hh+5LWAonRZLOKtRfwgY0ptk5guHJMimbbZsUaOMfXcHUdPOzaJ7aRth4346kS1aB+Aj0gfbOlmrzlRldUI31Q04Yq/n+b9/ebk9wAEnQ2cca86YzhjkDN+1PmDY7wESlox58pNp+qahkvx4BxL6hhgnxHmugaitkU+N6/Pux4A8K7WX2B0VlVdrftdHo96EVQ/aDzMZN4b6YztzcF+kMYO5wz0ypMxy690xuFF05g8cv8+ulxegHdgEntKWoFhKGH4ndEbCPe+6gobXL58aNYePV54OJvXfO4JQRGLYwFRnaSGCVEIJeVN1GOUhTB2x5TFWfqMBImetDhSfTzwl7UuTX+o1tcFcgJF0lzoseUlYucGLfAl1HjalvPoc7Adh12g6GDBEkW8CFuzOcEVTAkSDqR6Ka0BSBRVietmVGmMFULhQMru0kjw9sMdIgGxVMbWimmNrSjxIgl+6Mu+OG69WmZ7IFgeippQacwthjJmje29bFTDcfRsl2jUAnY8CgD4YfoK9zvBolSt9Xkioz6q0vO5lBVRlDSw4C+8kOZKMKnPpCnPmMD5JyXRPtUg4Gi4gescImL8ZMm5AIDLoy8ERHcc8OVo4tEIOw7oKFMZJ1dda3fA2AByStsqg42cQySs40eO3RCcL/Y1d7KRWIo6s+VkvJxdfkZR6cVAznBSx8L9pV92P4xfiE/NfxJOdimuRv85eqp6/EZGHEziEOG3Uf5mxyNnABemSNM19Q+7GsWJxb3H1BJvlhVsnzNG1evEzVXWtFOAVR8GAFwRdZWdVSem7ThYOi04vlMFBNC472kfjp10MLs+5JwmaZuPBpuckXWt3ez3JObVxVQpoNz4MOvNoYLhd0ZvIPxlbT2AkXzdQvCUmPu4DjEtBKU5uOQ1DkPHPdqRxOiyWChP276jXZjF5NgVAk2cnIx90QhRVobDva+6+bpXDdF8XcBdePprdRZCQ6JHRHl2nByltlD00oHjvWgLqROTqrKunIYOqYwdqgYuLZqlYlwUZebyndj+hCjB1dGbxjKhojU5qzg6ox90HSMWnxuqYpMmLwtwIwWLpgT7V9fazS4s3Rzf4L1IMnRQy8qJs6iRJY9y6hu7fkG8Uo3Bzy2Igeyi7KWfAAAetE/PbluYgqueC6eWq6Py+r9V22FLDwlyG1WRIL6EUeHzUr/jVLHVkkocRVU1tLgoUDBqW/g5omnlUGQSAKDaag9cD53DJZWxecqvVwNaN0b0Y0d1MFFkizMeyAnNnaft8Aq+zV4OJKOenG1HPX+yzVnKKJMe8PbISxhvZWtz3fASLN/5qufOzb+qs2PZtGCVAjXSx6kCc04T9fhcLXHuXgfYGYKxDgTnG65Pag6r+uwBvJCXxIkWi1jAGZ8C4MtJV96lKc1c2phwxwr3Ptra0M6+78l5kV/P3PwcOg5wONHLRm/rDOX+IlaO3eQ/Fs0nnOhp1LLYkm3DAcPvjN5A2FSfQDRiiSMjb1Ss2XsU0YjVZ3nNtIilfBzpAp+oWFzulg4tXcnAYqcQkmmbz+8oAKKiFVOySIdjtHXxyl43J30wlUIygolKdDNAwVYAACAASURBVCbTYk8pUZ5N5XX8t4fecaZoPG3PvWgD2/o+b6xrYxf6OmyqT4jFpgBg8yHXCSWlzVO0SpqOsKPRjexIouSH2no8Z1QhbKxrY+l2fvjPqKUrheXMglTFmPI4G5EDqKZtcIEUscBSSNO2E4jS+KEbA/5Fjt8I5qLEgF7xXdd+1AJQ/zrSiOGg4zJtyhTDlYyQfBXn/Ha4c1O/k0SuMszzqo5hLmokiZJJ6NnqkOMYAKpzlJueiUpJP/E5jPnvv4hnNLj/c4YNvQuSVinW2AtwZmQTkM4fb8lMMI8XcCP/6jWw4Bd4441GLuKb1kTKTHm56Yyj1a2wLARSYywr50jIczZmm7Yd/jmgfbhI8d7mfIVdy8ngxyW3AQA2X/SHwPbq+JzMUPFFtHZlkKiMDYB/RlWjuJJZW3IpGSLDkqVkW+w2fgNQZc5xQm+cc01dM3DU7kjEAmrmAADmRg5hDDoC1yWZDtYZt2B5Tht1/qI+q5oBlpW7drr3pJoj7c7B7glz9eI9Rk557lzp2tmOOXDBqkjbTiiH9VDC8DyrNwB2ZhdyQ7Ucy/FEfVsPFvZBtFJdZEQj7ktEGrGjySosjTlspLWhrVscpfKDogrcS2EgQDWGz54/boB7IoNuGGw91M563zmQQIf0/pH4lcSB0pZ1gEjH69iKOJuLqMOY8jg6hBRpINxzAOTGg9RZRC/tScJcaYnIlwNayMjOkyKj3EIlr13HXTRz0VvApbjpjKUlzD4Z22EXhYDrGNGNAYkRnP+9b3vfR13u9qjELgDAodJZAFxqv2o4cswDdfFOkRZj1JY7F6VbqogUEHQcLZ4cvL4SCrDa50Ymajt5bP7Y5Aw6tRQKx/oIqvgygkiqU0Ex4rnIpL8/bU72986mwHZcHfj2nhTLaDjSQU7i/P6YjMbetM3OrxTR1uUmctfTAbS1o1MaoTb3t2COumVZXvSMG/GUTkR5zmfvcIXZXrPnoXvyqYHtJbWQg8rbBuelpd+Gc6ypcyCnM8FH0fP3EwrbiyLC6jbc3CLK6+WM7ex+fxp7HQDgI7GH2WvFOhSzY4XTJ0hnHFbkjN4bOr8qR1Wmbbnr7jFySsMxbAB+jPemB5dmS19ixNgdovi/1XsBAO8/deaA9mOwY1O9Gz3iJp5jxdoDraznUwVN/JSzK6VtAu7CgFtIsMfJvm67Uxm0CxfjfjQkssaEMAe5v3H3S/sAAO89ZcYA9+TYYFlBNVkdaEGcEm5PY0tCO6KcVGnu8K6mTjY3UoetDQksFhrpjuMuCMLk+NJLWCd+pGL7YTcnuJCnmjznJvVy/+s/lXGwWGOUqiBjdwFTokdtm1N09f/VqmFr6BYnHN3VtD2gv1ZaerX2GHz7ZVv/BAD4W80H3O3YaE9wP3VhZmIc0C9cRFZdvM9g6p8GorZMH1VqJVviQ/mOy2tVryufH1zYiFcjRSSGaNpvkWLEF1K0fdRe5X5o3BLYjqtDWhqLGp9vtR63J9Cjubc6hW9AT+3m1vomxdnGdpd9xd2Hgy3dbESO3rVc+Zu07aAkGnHn5+4WLK53VX+vTX5BJKrEXQt1zcG1o1Zv4NhaXJ55gB3B3ArOgFLPnctf5gTJ1L6TIyS/T/nbcNeZmzMkhjTN/c+OvgQAcGpkK0v55kQdiYWl00zgvqdhOmUs74A1zc0mw1XrpDS0x+XGA/w9GA4YMXaHKB5c74ounTVvaES9BgqPbjoMALjypL4XOPJTRySgFwA3meoQj1oF82/VOdx25JFBP1o63cW0tEROf4PydS9cPHGAe1I8kmkbXcmMuFwOUfxmMYtwDhnH0VJJVRCNXqoKDZjp1Cri0UggR0iFf6we6QjSHE3YWOc6rqTia5TfV2h7WmRL6+Zuqm8T5zJTCkKh/F5AH70l6FS0dX3RGRq6a15tYKnoRPh0jhOulvObYjuBf7g1W9dXngFAlp8L5BwctHUbI9alaidwDongAjjYd1H+obINt3BU6fYSoSsJHZprR71vM5nyMDrKqPd7gfPcbGcd63WvFmwbcI1KEwtD+rwR1PrHfujYSBzThG4VV+aGjGYuFz1iuRoaOnBO7HTGca9z637gllkAgGczS9GGUaL7yN0TejyIrsrTeoNUXBWcwF5wPAbvkUiwjZl7OK0S1WnE5d6qx+NU8Ll5SzWSuWmP+tQRrcImeyZOjWxFFTPXcWO1vo0cI7w2gmku1419zkFHCKQE+P7UObtNJC4uXQAAljFiXMMBI8buEEQ6Y6O5M4mpY8tD5dS9EbFm31EAwMkzq/u87bUHWzFXaMQA7ou0tjJYS8+EQ209qAphVKczNpJpG6Xx8I82TbSDoWazbTs4nOjF2Ir4kBZLoNqC0jI1+4+6glMqdVGHHY0diAtzbGjBI8nJIS982JrL0ogn4NLmC9F7/SCKnZSGnco4GDeqtOD2ROeUUpNHl8XR1i3b9nBWxITLh+TQxSzACTojlRM1AkwLIL4d07ykM1q48i0A39dfRb/lflh8JWzLbY97f0nUhrlIufrMcO0E1ZiD1yhImS68uFdr2HLbSCJ5XPRePS8u8lKjlL9T66UChZWoC6lFb3JmuR+Y68rRL3vTttGg1Y03XU5+CZMbWagtUwrQnHF6Wqh6Pek3LjpP0Bk9EcsCDr4CAEiUTcXHUzcCCBo8HLOEoxqrx+FYQ+r1YKOhElE1Nje+OKeNhOrPRc5VY547PpeOoIIro+fvUy/c90ukq9G4nQodU8/0PtG1t1SY7qRCNz+b3jsmIbjhiKG7inwD46/rRlSYpXh+ZzPK41FxmZAwGFMW8xRfJVh3sDVU7VuiW3Vram9yoNySEOVOPVD+5wSNGM3xxJNb3RfOUC45BOSMHanAVo5SKzOOS6N8jUsOxCyQ0OjThvw5bfuMkIcJpfGI1rvMIZVxQtXK7klltHRaPyjCI83r33+0C0uFdZ97s3U2C0X26b4vmKDvg8545cpiAHzUEsjVBw9sb7jXumPr9lEXrZPRjAqrFygZDVz9S+97bm+qTWo6jkTshqU2qtFMwTbcVCpRWg4Yl8w2qmORM87GKHmUXM1x9f3GGfGqAzQooBXYhb+/Wx8KfMXRVgFezEh3fO97zcA1LcJ1v5mMbZOYnrYPhn10z0JHbxp4+WcAgIeW/xDdcOd2lcbNncOCicF5Q721XJ3XQDSUYfOYSk0RiO3lB7fmEZWxEom4Be+X2nfuOk2rKfxemDs+eC39fbo3c477oWlrYDudcCCgZ9acYEhd0YmAGse45jgm9gQXYSfo+i0t7TfUMDzPapjjdy/vBwB8+KzZA9yTwQ160XKLg2OF4zg40pEMJR5VVR5nqTQ6UI5SmGgZef25ib0QaOIfDGp8v35xHwDgQ2fMGtiOHCN2ZPNGpbRkEmGS1m4+lOjGyhky1gJFALkojAqKMOkWsSroWePqQuqwsS4RirWwt7kzlNrz1oZ2SLpPquoSejRFGaS1ivdSaagCD76nUBvTb6erA6xbnHQytUcBfYqDSYU9bE6YSh/9Tcl/uh8u/Fbe91z5l7GMUJio1qxg4awuyjlHkUqHlgjicHm9QcM6uI0aceMNAkHEOqBEHdgkYFirkSeOIeA/lk2uia6jge10qS86erGpFJtuHOryw00wj+nwOZAm41n3iC+cNNqL7LaV5/QnJCWueBqxmsMd3CYophlsm4sIq06thUwJK66Um9p3tl60gGosYVFIGBscM6qQM2erPd398NqvA9uZtCukDj8/dOr2Joejjh1WjJMS0KfWjAhUjWDQYMuhdliWPPrzRgVFB9+xfEqft02UGK4enw6v728NJchDxeKlYkVAbkEsKbei4vX9rZg0pixU9Lm/8Ox2V/GTqwU3lECF4qV50KR+LM0Hb+1MocMQPfGDDDSJIU3Gq7QmNHm+TSJPKipLoghTnMpxHGPOnIqx5XGWzqmCIruSHEJ63k10Rj/oSSqkCE1091Rafz0WaKLDuoX5fCYqBBSXL6bTGdCJYPnTOyalDmJuxNWYwMoP5m23hKHJc3XLg2rDhaNSHNQoKSdcqDqDuMiZKpImiexyolGSEkYShd7gNqqBHNgl4BDlyh7ls0AsPBE/l637pFvYq9ebYCqVphtrJtaITofARCU1vbt1hkArkyvu9UFjjIxBJ2CngbEz8qxAiRNDlucdPKbK3pHUr+XuCR+hzf+bG1tcjjCV+TI9pZzzS+0Dx0xRryXHXuKcAn4H51pnnvth45+AVHd++4Z3Q1inDdff3PfhDVdjKUHDxdalq5FI23DDiLE7xLC7qQMdvekhH/E6HnggS/d+2+JJfdqu4+QW92Eiu2FzYSnfIkyUdn82khSG7kkYU973KszSEgR+0DmcOa+2j3tz/EGGlG4hpIIiTZJ74cCB7TiYb6C+ets6uTxcruaiui3RryXleIDc9pyhooPt6EWUOHQlM1gxQ57TtLGuTVRGi1Q1JfR9WsRJ6NqOk3uGC6VRUETcJEYX1Sx2OjVpDlKaKP3FGTuA6xzxG2X+BVQ1k9sI5C9QP938TQDA98tvCBhKUrojUfVoPuGUeanePFHCyZjx95dYK7QNZ/jTuTrK337k6oG64BwFqlPphImFxXcktYHZ/kTovNy/WxSjjFtc07GoNYmxVVESBVr2Ah35eY06B4q23rJhga6bQ0zPso6VwdGAvfYMDki1lA5hIVMWi6Az+E+0d7gfTrsh7/uc0JSLHiZfX73XXH1vNiIscJBUCFSdJerg3LuNYzJJ1iQnMpFktV/kIPPX4lVvP5uPzHznTy+yEcGz0Ww5qP97p7EPBJMxaWJF+d8f/jZ0ivvFQu12nqiV5pykZRKHGkaM3SGGnz6zGwDw7lXTB7gngx/P7TwCQG5oFIJ/aqAIkzQK6jgOGhI9rNy/DgeyYkUmD6EKWsRVFzBoOLy2r1WsGiyFEyJyR7jzH24tzg+eNqtP+3I8oJ7ttgaXhSGNtJNX1TSu6JdUxkFnMlNQzIq235BVMzaO2exPlDcprYVLNFxOCIWDA9cREEaRdXdTp7gMF+CW0NJRf/1IZS0ESa4SKVr3Fqg/TJd4W0MCQLBciAqiFBpzDDUGJ6d8DOgjATrFbHV7WkyaIgf+yJ1/K28f28aMlPvOerT0osD+UmOXxiyxXGqYRT9F4yjaPIsph0Pt0Dam4eeV9RJQi7nawOqzwxlDNLfT/eci5XQ96Ce2pmh2fqF2Ak5Y5hYWEqzivjsSz+qErL4j73td6otaZ5euv+k5LiZfVp/na6ozqjp93L/VudpvVAUUcQXH+lD7ne6Hitq87dVryzFuglHU3D5kTLUyebVeHers5pwzRsKW4B0rhWnFfEmx/O8445fGtr9JST66KsQkrStM50LH+3r5ze6H/avztguM1+z2JgeMmi/rH0c656dKG/fvoyu5F2Av+E596liD8zRkaspQx6A2di3LutWyrK2WZa23LOs+y7KGp8shBB5c70YruZfrCHLoTWfQk7JZKfu+QEN20cstqFQ4cLwXU5h8CFrciKO0jqveDABjNREXEzKOY/RUHi/8+bU6AEOv5BB36fY2d2JiiHSDWCQipgKTgSmtizymPC42uqkmtLQvB1pcx4zUsdSSjWCbameqqCyNiiPNgGs4SJ7/XY0dAIASQ74sPRgUqddRhFVUZA2eKpPzyco9tybo5o5KjYOhSxPx1UW09FG48Iq63j7rfw8AeDRzMmLM2JMotPpBGR0UUeYcNzRvmioV5Ixd/bmRuBjfx/y/yUnh74/EKKBt6DngnB1E66XIH1cyRzX01WNzarWB/gkMm6fGXul+UCK7ugi/TvTNVC1AZzSacnbLNErNxnxGzXFMr2iT8awKThFKYlFgyknA0nflfa/Stbm0NIlDgmOXqd1cxOh+qEPNP3Zp7Bdy6gEasThRTr0skqzO+ZyDSnX4cc8aN2cHrm80Biy/BgAwOrHD+173HjQZhqa5RWfsmpydOiGqYtgGgN7pPZSrX5gw2M/qMQBLHMdZBmA7gH8b4P4MKOpau9GZzAwLemd/4/HN7sv4wj6mMBNoIalSgVTQfEL5klKhIgDYmo0KFcqx9E9ZFMmS5Cr6kcrYyNgOxmtKiRwvJHpS6EpmMGd85aDIHT5WJLpToe7F+oOtYoOOFiSTqmTOkA11bWz+IQeigEqFsohRoMvRI5CnmhbsYdIAMrZTMPfVj8b2XlG0lhYeEwSGOrEtpGNzY12byMFAxohqNPiP42hyAnT0Zl36g46iqcvZNZ2qbq3Xk8oATduA+z8JALgl/V52Aco5O9Satf7dKO/PlJ/rGbImo9kJtq2CqOW9jHGp3n9TbqUxQpy9d9TnSQyVXo1Gm6j/ZBCq11pULkZgoHRFxwIVtcC63+Z9r1uk6743OXB1NGaTgJwuNcNU2kw3L/cYDDxTe7pcY8uygKqpeYMtHrWCNXWZeyTJveUMmqCIVbBfqjHmp76TI5VzYqi5vtwjJBFa8z8PJgdVsM40J9qVvw2X+8szNJjnZOHFAIBxTS963+sYTiZnrc4QNqknm41nnlXQ15HYNkZbYDhgUBu7juM86jgOzS4vApg2kP0ZaPzsWZcO9v5TZw5wTwY/7nn1AADgXSf3z5DZnlXZlS6+TeIvOtDLPcwC/3CiB+XxKEoNNQk50KQ9Txix6i/c+czwGuOJnrRY5AlwvdjSmqwNbUSplUVHx5TFxbVkaWEtNbypHAWnpMuBIplShVXbcZDoSYtf7NSuqWQEgWobi9SYs//PFDA6ANdZIKkBSYaVKVdbR0kLmxepyzdeolF9bzbMXbpI/oyaCuDhzwMAnqm8CLucqay4D7eYVinffiOMxqUxaksGsWCsqIY1l/4xw+CgNOX1Ur+Jymt69nKRZvl5cQ4XnWE9TVDiS83z5Y7RncoA40/MHiw3T4VVpDWdpy7qO2UsP9ZikaDhSNBR/AG9CKDp2TaVStM61piuSdTEgaADghNn4vZTaeWcI0PdZpEvQkhjjYtWq/NHwqe8TvMtSxmOMoZlFmRcc1Oleq1MVH8Cx+hh0whUmn5HLzDzTADA9P33G/cFzGX8epkSalxf/TAZz7r3UzH10UcbDG5OW2A4YFAbuwquA/C3ge7EQOLPrx0EAFzUT9HK4YSnt7lqvmEMjTAgr6hODl4FGSZhcnYpxzJMXmNXMsPS3AqBSt6EyQ/uD9z90j4AwAdOm1Fgy8GP7mQGGdvRCv9w2FifENd7JWr8fEM9Pz9SGTtvQWMC5aZKx15ddvzEhTRpEnqaJxRfozI6UvVzchgsEtCYqU1JyS1yculokyo21iXY8h0qvOtXRF1QXbRJNXjIENJdQ137Sw2CJbqoddyygT3PAADuqvksAF75mMtD09FB8/pqWODR2OoWUOTVCKNEfdafl07XnnM40UKaFudzDXoItmfI6u9/JsMbxFyfO5Vz54wddaEumXfmjh8FzHBFfGY3P+t9r1Mv1t0D0wJdVxpNd21M7yxTigeXVwroI7SAmemiOyfOOJOoLHPfce8SSUkf7tqp0XD/sTIahgAQNOr99WTp2ePeSaras98JSO+yEjZKrcxjjA6IKP9YsLZZOGk0UO6W8rN8jjDdcNXl0QL6ShKmbsw1rA91xzKVwdMxHkz9DlPebyhhwI1dy7IetyxrI/Pvct82XwSQBnC3po2PWZa1xrKsNU1NTcer68cV7T0pJHrSmD9hlNGrPYIclXf59P5L8d5yKIFYxBLn4FK+VXlcrngszcX0ozuZKSpP+VCbu9gOQyvta3Ql02jpSmHC6NLQkenBiF1Nbi5omDzTinhUnMe650gnADmNaVN9Qpyzm+h2X6CSaCeQi0yWCdunvktUjYFc2sA8oWFPOcESY9123IW/hJpM41KamzyqNCaKLhNM7erus86BpntP6GjMOmPCVCNY16dJe+/Ldu48OFY0274sR0/tBxf9lzj0JiuGLGeoyESa9FEycsKo1E4/JLTqtBfZ1W7iicapfeSMKVUcR2JIqefJ3dpY1AKWXg0g39jVRZB0VHq1DnOhvgL6saZG5yX7mH4rtm6prj3OMcbNNZKyO8XmuLN1bw1t63K/Aa6ms+y5Vucj7rnmUknU/ThRKMl1khjEpbGoe7HmX4iqti2ogZtKVlupy9kNPx5Misum8aUblyYnlW7OMdkQI3V2+wmO41zgOM4S5t9fAMCyrP8H4FIA73c0SUuO49zpOM4qx3FWjR8//nh2/7jhR0+7CrXvOWVEhbkQfvfSfgD9R2EG3Dy/MEXudzW5i/vJISjJG+vaQkWCAWBnY0dRIlO0aDPRW/obd2Zp+teeOXvA+tCX2Nvs3vMwtYLbe9NYPFXmrKAFk5S9MKo0Foj4mFBdERc71hI9KYwpi4nFLeiFKjUaKdIsoQRTfwC9eJMfWw4lxEY9Oa2kToNkxg4V2TdFl8OKmuguFVe+BNAbWqbFD3cdIrAx+/mb3D8u/o6xHQl1k6tRrc6j3DmpyuAig09QsiTCGAWSUmES5WeahzmQQ4mrYRo8VuHFf7DskbkN77sqdw0yObGObSfvGBpn1gLDAl3Xno4GbnJomZwienE1/fl0a0TfKkqi2rmSex9z57KIYYAEnDFMY6b3PTlIODaH2pb/vM05tILyYYIyVhxdXFIrm4vIq2UdWUV36J/jQB8nLwMATLPc4JmWuZDSO210hvASw/vd7JwppjYv/5spGjyixjwAsCzrIgA3AXiH4zhdA92fgcSPs8bucMll7E/c86pL977qpKn9dozN9W1iuqkfJvqIilGlMaMgBodoxNLmIplAYlhh8oP7Gr9e7VKYrz1z1oD1oS+x/bAb2ZWWc6KSMBL6JQDszUZHTbQ7Pzp602wdQw7rDraKDTrArY3MRZh08KjDBcomEYiWLHX+kHEsGc+jhSWKAGDzIfc5kXq/dzZ2iESyCKaFu46Spmt/qibHUY3g0V3TMQq4PEECJ9D30ehD7ofqWcC4eb7jyqJSEkNNHfOStiUCTNwQpnI+3D4ZAf04t1/hbSR1p9XI1vQapoSLcl7c4rYQ1dUffSQDO52xgdJRwMSlqO7ehyp0FKV4rGML1FaWaI3G+Zp51JSPzDlKvD5onjUud5kwZ5xO9C2ck5h7J3DXcZxCNeeMF5PDkOZk7jqox+PeO5xzUX0vSJ1Y9DzSsOIF2wTGLvMcq8YoN97VOuosq4SOP/ctAIAros+732vGpEnQU0eTN70PTG9Q3WNmiuzrfjM5yE2G8FDGoDZ2AfwQwGgAj1mWtdayrJ8MdIcGAkThG10a03qYRuDCcRzsbOxAWTxSUCm56GPAnfykYj9ALtfPpMSnYmNdIlRUCHAVaKs1apYm0KQ4RajsK4WBYZaHzt40jnYmMbmqTBvBGmogUTJTLT4/6DkPW1ZMkuNIxlyXIXJEcOAqoobJ364ojQUWZibQ81Aald1roj1LPP8A0J2iPNzC7aczjjiXeUxZXBwFdhx3nlCNJROKMRzCbq8u8igXVCe2EnYOenv0ZffDtX/P+95/1FwkMziHBoRsJBRMgyFLER3OiFIjXpyojHpd/H2W1OslqEfnnFTqeal5jkBwES0x4rnSKbQfLW7V+sv+CDK96zxV2kVultlHYw8F2pVAxwAxGTs649QodqVx2OnYDQCweHL+ePdf3mLq+XLvQP89o48SVoFEnInbnjU+le84g50zigK1lxlnWFjatOk73TZ590XZj3Nyqm1z77c2ohhPOwUAsDiyF4A+j7xGQ28G9OWKTKk7pvWXLs1GnbP8W6n3lJowpVaZtAWGMga1ses4zjzHcaY7jrMi++8TA92ngcBtT7j1vv75/PkD3JPBjye3uiWHLl4yuc/b9k822w93sIIrOnB1GAuhLB4x0tpUUJTB9CLXYWNWDKuv88Glsb4fPb0TAPCB04Y2c8G/sNlcn8Co0pg4QlrfKlMopiF0sKULsYhV0KCzLMuL2hUSSyLDoL0nLYoyERoTPaHqCdNC31h/1gdaZJrUUP1IZ40Y1ZvPoTuVEQtxrTvYqlWF9cOChYzjIGM7mD9BFk0vJHbnX6z5n1LdOeroaOqCOZU1xgPKyF4ERlaPlOa2FZFdcCIxYEz+HNzjW5RRH+YwOZ1k4FF7pkUxPQuSkiXcolwVqOL6ozrfOAVpioqaZk/VUOJK7Ehq35rop7rvuLrrVCaMqL7qItfvmLW9d0t2rJ36cQDAp2J/QTKtd/qqdHNKA9Dl7JqMxmJox7rfOEPHiziGMbizm5qqLfjnf9N4lZSH4vKTTedvG3Jv1ZQlztklMT45hhvnzAjS5oNtpzP6d1/CoPSstqXSmoHgc5PvhHR/m0HU6mgcthXHCstlVKr6KdSSyXBVxwodvqldP1YC5YUMl5+m8dkatgGg12cwUZU5kbDhgEFt7I7AxU+z5Vg+NEzonf2JP2UVq/vbMRCNWHLhIcc1fFTREOMu2VIrS6fJoyrNHS79ipvoC6GtOzWg+bq/en4vAOAjZ88esD4cK1RHxr7mTm3NSA4USZkrHCddyYw4b5wWCNKo5M7GDuPCQ0VzZxLtIZgOWxvaQ403ujZSxwGpn0vP9ygjDuMH3dkwpaFoUVNIQZraZvPMfF/pzl13jrrtVXaJSYzG9D3X31XWVgBAcukHvO9oK7/zxDaUB1INHu56S6I0KhYwTodA/qEgujZudPCZTgtUwlURK0m0TVIbVJIzyQ0RymmmOURd5PqdKGk1Qlg2Bs0VcwEAt4y+N9g49TWqGhhuO/M1Je5MlHmdEJXp2dUZz8WKWumMvyUh2Q9s3VtBhF5CR/Y7u726y+zcoo6RYKckxi43z3D0XvV4tF7xw0SvpXfAAkakUCY0V5gR4Z8bD8x4B0qtFMagM7AvzUkmsSndtTOp2+scLRxVP5M11k2vuKJE2EIyiIYKhudZDSP4J/IwJWjeqHhs82GUxiKhRIHCojvllpSZVFU4akTzpCO5uwAAIABJREFUaVk8Is4LBIC2rBquRIyEXiJk0EijX370pm2UDhB9+HCiB53JDGbUVAwLFWbAdVa0dKUwURBZJOzOUnWlBl1vyjaWKvCDvOLSsgKVpTFx1BVwX6qzxxWma+eeh2go45jyn6X5ybQYGSN0SEkVzFs6k2LFcs95ISyv1NxprsWtc67p7qnO0aJ+T8JHuvW/lO0RSffg3tKvAwCspe/0vqdm/YtpU2kTda3F0n2V/bhojxrx5vLD1WNx6zzJIlldpLP0Y4GRql4P1oi38v5jF9yBXE/mxKhtW3Mv8uobM2WP7l/2IwDAu1P3AzsfD/ZTc1y1HT9MlHldbq5JE0HnAzG9Z0wifrr8WJOwHEvp9X1JubJcrqQkiq9+47+PFHGWpK+wBrjg2efusWQ/bs417RdwuOT1obCTSN2Pq8Hs36anbAIA4E2Rrdo+mZhSOiekSd1e57Tj1v45irqpXB3/m+numBxBQxkj1tMgxw+fdOmd/37xwgHuyeDHa/tbkMo4uHpV/6kwA8C+rFESJoK6/mAbVkyvFm/f2u06OTgvpg77j7oabqoKqQRbDiWwckb/lWoy4b8e3gJgeNH0qXRPmPxbegGpJVN02N7YLqbFkxede8EH4LiLpCkhxMoOtfWgMkSOfDJtG1UpVdCY5uiYHIguOUYoHCN9ZhI9aZEDCgAasiJZ0syF5QaPvwn6Ui3h8iIDpc4MebVAzkgmzNj0YwDAdnsqSuadm2vGCUaOaVduLab2m8u/q1UiRycw86Qk+ltMHqFkGwkllWtHVXXmopbqe4ebLwJGs8FAyGii7P6/yTGVZ6SV1OKbqfe7f/zmKtZbUlHKP1e6RbiJFqpb1JvmHZ1xanII64SwgOIiZZzt0OJzUJDRwhntqoOGG8MTlPQRfx9zVNfCTlGpHoIKyXPF7hcyZ1eSf0w1eLm2JcwK/7U7NOUCAMB5kbXaPpmqD+jESE0OZ917iCtEQ/OviYmge/+ZVMqLCZYMBYwYu4Mcdz2/B8DwKcfSn/jeY9sBAB86o3+vVWsREdTykigrfqID1QkNE8wnKp3IoFFQGouK6Zl9jae3u/L+/amefbyx7mArgGC9SxO2ZBWxRwmNxlGlMbExR4tVSWQ0mR1HSSGNmcZdGGGxutZuMcUYcCnbYdShKQogdQaEKcmkE3JSQYsz6fZhzs8PnRCfTg1UFz1QI77k4Z+syVFWx/b0LT8DAFyd/Ere92Qs+yMGRMHj+hJQe2UX0/l/sxFZZT822iMpdVJERJatM6psQ4rhfqiGG6eeXqnQ0LmIf0BEiMkTJIOAnl/Jear4eeYS1MONgOGZbwd+1zmEdQ6jYuqWmnNsdZGy4Pc0PE0GjI5dd4Sh5JqO5b+vOmcDEJxTeUNP/bs4I/IowxAw6TDQ9ZIIyHGQUPT9OgaSetXkZGaF7wzjm0oI9fiMwK7KGQCAsyPrtccz1yDnx8rRTj3DT8feUR2LQM5g7TEYrrr3n4kNMVwZpMPzrIYJ6IGNWMN3APYVHMfBP3YcASAv9VIsKIKqipvoYDsO2nvSYsVXICdWNLNWbixtyyrchi2DkEzb6E5lQvWvr7Cxrg2tXSlcedLUUOJdgx3bGtx7sWKGPJofVrTpYEu3uMYuQacQycEU4fCDDEVTmQ8OYdTMARml348wDAcuMqiDGiEoBE7QiEOhvOE8OA4icK+HTuFdZxwEIrhZqIvyFNWQ1RgseQva5l2I2ElstmeiDfnjhuiZfurokXb3XLlyJ2OVBR8r5iOgd6r7SUSBelPBMaZSkrn7FBCWEsxlHGVXFEUuwog30fQpt1w9d05Ih4uyf2/6be6HF36gPYaKmZq5wnTZdJUoTHmTOsOV24eMB5OdpqunvMxAv+bea3nlqwwRSwI5iiQCVY3MfZPQgxcw870pfZOeXc5JLqnVKkmP8PebSkKZ+kTXkksdMc0HHVmat58xko6PQqMzFjMiTcCm+9njJQ25+uo1oFu3yPAs6hw3ppJ7xaTsmeyJkTq7Izju+K+/ufTOGy9YMMA9Gfz467p6AMBly6f0+7FoQpUKVBFlSSomBORevGFKFfVkFythywfR4k2aD9mX+P7jbjT+hvPmFdhyaGFXU7gau4AbvQwrEhamti0QjAqZEKZkDpCjkEmxZEo4UZewda3DOAJM9EkV0rxnglSkbJmUxrzmLpxx9zzsLvsA9pZdA3ytGvhqFbBvdd5mXN4ooDdeVQONIn66a5O3gN7zLADg7sz5ge1ojPodLbRon80s4iQiTSKKsvIdtyhXHRcc3VON0HDbSIxU1dFULGU6EMkT5D2bjB0yxqYrBihnIHPHai+ZCMw8CyiRO7t0z5DJAaZzGunErgA9S4a7h1zetwrdO1LHogD4/Eg2f11gkFpMa+q9lThR8tvWi/8ZI8LZZ3hmbfC+6yi8QC5KmWGilep+/uOTYWmK/puuZUCd3ecUIKaJuqa7Nvl598MjX2SPZ6rxrPbBZtI5VOhKHJmcZ2GMU7r2JqdSmDXCUMLwPKthgt+8uB8A8OEhrFB7vPCl+zYCAP7t7f2f2/zynqMA5IvY+tZuAPxLQQeqQyopnUIgBdoyRojFhIMtbv84ldH+RCpj4/EtjZheUy4W8Rkq2NIQvq4yAHQZKEkc5oeISALhXmRh78mMmnAe5kTI4vVhI/9q3VATTB56FWEj2FKmhankgyd0tv6PwIM3et8/m1mKN0c3uH/88iLMA/C7+CJ8JPU5Jqrl4Efx2zDr1/8KjBoHVE0Dpq3Cp6Jb8Iy9HJUlp+VtTdEvXUQ9T1Dn7zcDAF6wFwe2o4W638CRUBIJXCRVEtmNK4v3CkFdW9YAVe4LRxtX59wWps9qNEVCh5aoVXOL3dps/U+PalpELiSXI8sZqdGoBVRMBvY9BxzZAYCZk+wMYOXa0xksJoaFzvAyiU3pnj1uXqbrYHKS6eYgk8HBC1TlPtP47jXM/dQ3LnUqIGLFOT8MzxlNfWHr3tqGiLRpP6onzwnGqakt/nZoLJuYNR4lXJBH7Hfm6K7BJmc2ehBHWeKgO4Yj+WOtLISgpiSCrxUhNNGlQ7A+iS1q0qAKywwcKhieZzUM4M850OVkjcBFW1fKy0kMS+ssBjSP6mqYqaAapyYvoIp4NILetM1K+OvQ0pXEmLJYaKPgUJtr7ErrgYYBJ6xA+PXqfQCA9586tGvrctjd1BFK4IkQtoSFpOQJIexLLMxLFJALMRFMyqts++GaDxVVD2PYh6V5FRLuokVQh0ExdRU2A7d/ATjq1n3ct/ILOGf1CgDA3m9cAmx9GHjk34GWPTg9uhmboh8GfvUrYNQEYPRkIF6BvWXZnMoOAB31QMN6YNvD+Nc48K+4B/jfLwGjJgKTVwCnfdLTGFAFcGh+8SJqR3YA6R6k46OxpydY35xb5JnyFFVwEX01CsVRbquz6QA0LjnHYXFlfILbqKrfnEprINrKPDCq0cyRK1TlX84ZQNF4In6YxmzuXij9Yx5/zmiybQeYdwGw4R53TOHM/A023Av86cMAgL1lQINTjeTa9wEHKoFUN/CWL3ubmlgTYUtjAXojn7uHNE5VJoPkaTcZk5zTwC9QRaXJTGsXk3NIpXdzfgSTAzxXwqbw2PfDpI5s2o9eWeq84m+L5hjuuVZrbPthqiusGtL5zjeKGgf3ewnLcA5eBRq3AJOWKG3I35HELFGP4T8P3Xg1OasCpZ8F92y6YS0qmZOHIkasqEGKm/7kJsXfcN7cAe7J4MdHf70GQP+r+dIcsq2hHXPGVYonhbpsZDeMWm1HbxoVJdFQhmtv2i5K5KYzu8gW1w0OAROx9a7nXPG1D50xq8+PO1Bw4CCdsdHeky5KXbetq3Depp/KZqLwqegSiDD5h1uNMNeUEMaZAyCUYBsAzA2Zix8mil3oufFfF5EAXHb76op4wdw0WgTpSjfFkMYdyS8DRwGMnQG8/duoi50CrH4pt9HCi4GFF2NnQxu23fEuvDmyAaP3/oNt79Ant2PyxImuez/diw985fv4RPSvWDW5BGWNrwM7HgF2PII1AB6Pr0RF178D6QlATC1nkz2v37uKvDtO+QbwZPB4JMTkr9ucZkrZ6MDNgURppV84vYFihKWKVmO28hfgXJ8pmmmKtpIDla4UN/2r8zRnWFMfTUJgBG3pIYHBAGQX/DOyrIBN9wOLfcZu637P0EV5NdDdgklWC7DpR7ltVv8Q5SsfdtsyUFTV/tB1LGZxbq6lK3+HEuvBJO7oPxIZen4HDjkkzAap/nlRgyHcNqZ3O70XJAJZeX1y9OXD1Lb8m9gGqrG6H5ffarrfREXnhnsgZ9e3ETmMuKyg36fPwTmxV4F9LwSM3TBjj3KOwwiBUusmlhLnNNCBnOMmI71YVe7BjhFjd5DiwfWHAACffsvwKcfSX3h5r0sr/ue3HJ+8z9KYvGau3zCZFpL+KJ0U/XNTmFJFBBJTCiNedKzY1dSButZunLNgfCgV38EMug2HsnTysFFaAFgZQtAKCLcwC9ufsSHq7AKynG//a1RSDsOP7mRI2nOIbcOwZ8IscCTOKvK2c9RKC8C7o8+4f0xYDFz/gvt51xG+sUgUN6Q+CwDY+9+XuKHB3jbAimDBV59CEnFsrx1PnQPiZXjOXorn7KV4+t3numInh9YD//dOoOsILoi+Djx1NfBU7hCLF3weJViCEivt5gpn0TTjIgCvac/TP7/UZ9kkJh2DMDRJll4roHdSOyS2wy0qyTDy6gUbDFl1H+74dM4dBhq/baDUmhbtBBp3zdlFvK58lL8/dC1yKrvBdmuYXMwpY8uA6lkuTXnPs8BiIAIbX4v/L/D9x9yNTv4QcNltmHXzQxiHNtz3ydMwfUINcIvL6vns6xdjVvwM1Ld+AGiKuUZyRyNKkhmcGzmArfZ0lHY1AHY6+y+DqpYNmGX1oL1bXmqQnCxcTVtCKoQxQu2ZVPfzDD2G0WCqOe31icrMCPQCwpYComuRZnJoTcrf9KxIRLPyy47JqcaSfH0/erIOVMm862+bngGO/dRdsxhIADi0LvBbmDUTXd8w6WwEk16FRAyP0GIQcyOMRHZHcNzgp24MF0Ogv3DrI27B76ryeGjaZbHY29wlVqr1Q1pShhCmbA0hDK2VQPmKYY2bY8F3H90GAPj8hScct2MeL5Ba91yDgqIOYRWKTXQkFa2CqLEf0px0AqfUakLYfOawauFhIsE6MScOUhV2ACgLwbToZpSAAeBfYve4H977G3FbHiIRN6IGIAn3+S4oODV5GfCFXZh98wM4I7IJ/7EqgxPKWoFXfg44Nk7bfiu2lwHY7dv5E88BCf5+0uK21Pcui0UspDJOXmkR3X58RNb93/H+1i+KPWEY5rypHRIE5NTvybgkg5F7RiWCUJSjaKqrSjAZBATbEFnz2skea46Apk99Jicrd55+A8Er1UPfWVGgpxWztt+Fa6MH8cFo1tBdeClw6fe9/Y6gCtaYSUB5BfCVVuBvNwEv/xRXRF8Adr8A3JE73igAv6JH83v5ffkSgC+VwvWvvAZXJGvCQkzZ9yr+O1aFKGxgWxQ44SJvH3o/LjAYD2FSoUjcSSpaxzEaJLmcRzrcNaGJnUMpQy3MPG+M7DuFc5UJ/rmDSt5wjDU1xctfOifnxAq2b8rZJRgp+dkpdKJB64Sut98gJrEsLgJ+tGQSEC0B1v4GuOKOvN9EVVLSSSAS88bKqGQT8Py9rkPHimJsbwqrrGlY4+j1ZkxrW93Y42455YWbKhUMp6oYfowYu4MQn/qt6x3/2JvnDHBPBj/ueMrNYbvv+jOO63F3NHaE3kdaUoYgrc/px6KQCrd+HK9JLp2x8fCGBowpixUV/RzseHVfCwDg1Nm1ofcNq1AcxmBcMT0crTqsKmMYsQ4gfDk1CQ3bjzClinRlTTiEKWk0I4QXn3OgxdKdGGclkEIc8Zq+eR/onnN1fnIQwfP2UnSuOgOYUQ1cfCvQshevPHQX2rc9g9GjKnHK3MnAlXe6wi2JJrZdjm6ay48UKKsaIpcZQ44ebUNCW1SD09SOyblM0R/OiUWRXQpUc1IF1LbEuPGirQYjyjt3wTaSR42Ef4j6PI8xkKNMNMyLan/yBeCOU7Bw/S34Mg2lz25wqfcKvPtuWcDF38aXO9+Fg68/ig+trMI5J05xjYuysUgkgVt/fS8cWPjmlSuASAyIxIFIFLc/sR2RI1tx5dRWTOncCrTsAfY9hzIA76Wp63euSjjeeSew/D1eOS2TwcQJJ+ng0WYN2+QZVUx+rB3CsTHLMJ/QmFvAaG+oY8RPmSYDXFQuyAo+wxKnqD/F5UiWbcDVcVeNOu4ZMSk9e9fXcC3Jqeh/P2QMSsnRiAXMOc9N72jZB1TnNEa4Z288WvHFkt8D37sJaDvgfb8GQLo0gtjD+e+lUQDupaXereOB9/zGSwtwMilMxFHM7mgFHv2dm+NupwE7he/G9yGGDGb86Ragemr22YhhYWcG95VswmSrGfjGtUCmF6iZA1TPwkdTY9EdS2Nx3XYgPst9NkvHwLKZtcHe54DnbwdOvx6Yc672eg4VjBi7gxAvZdV+vzAMo159ied35qh8Es91X+K6M2f3+zGKy6ENV/6FYKrD2Nf43SvuC+CjZw9PZ87ubNkhTjmzEHpC5rGGcYiYaJN9AUndRD8mjw0XCV4YMrIbhn0RRgiuNsS2umgtBy5veF6dW9/xoTHvwRXiloqDjoae9331LKyb+SF8c+Op+PDi2Tjl0kWB7XXR2ljeAt/932TwUWQxYyiBRQtW09ijbUyRfolRmItIBTciZwlFTjhqp9eOQJzLO5ZAbMYY2XX0fVaRy/PN/ztvG9+xKILopfSMX+BG+H9yFgDgN/Gr8AHG0AWCirotqRieslfivGmLgSWzvO/TnUn8XyYBAPjmyZfk7fPUP57H6+nlmH76Cly+Yqr7peNgT0Mz3nLbaiy3duP+qu8CPW3AfR8D/n4z0lN/DaC48i8cSFSuxODo8x+JaOX+uZjSXkxvblEUP7sNW0LIQOkPIxYXYxxWYevlUnTdRAGmKDXXsrEcUoiaxX5HZNpwLm1dKeD0y11jd/UdwMXf9n7Loz231eHEZ/4Zr5Q9nv0bwMSlQGUtUDEOv1zbjiRiuHhhNaZPnwmcdj1QOgqHtzyPDb/9opsy0tkE3HWhu3+8Ap9LdeFzZQC2+jpUOR6IxPEmK4U0IoimxrjpA1XTATuFic07EbdGIWmVuYKDZVVA806goxHX2h2u1ffKX4FXck1OgSse1+6UA7eUA91Hcz/OOmvE2B1B32PDwTbv8/Gi5Q5VvP/nrkDLD69ZedyPvSCEMBAALJsWPoIZlkYKACeGNAgIYUQTjhXfe8ytrfuxc4ansfva/lZUlceLyn0Je/8kEUlaOCwOEUUPQ9UFgOk14Q17Xb1XHcJOh2HqWoc11KVYxIgH6cAtwBftdRfnD8Tfxhq7fZl6IFW7LZQ/qBv3PNVY35+urLFryqemHDTT/CWpbykxCtMCg5gW2yaxNpOKLYEooqZh2dxROB83TGSXDCDPsOIEqnyGVG/WkZOnEzFpKf7+rm34xG9exdzRlfiA7lhK26a8UR0aEy6110+RhWXBiZXBQQRrnXnAzfuB+teBO88Fuo/i2zsvxRrrO7Adn5Mm3QvcvhJPl6Rhw8K0e8rzLtiyVAZPlvS44/f2nHI0APysrRvJEhsT/1EGvJSbi6scB0+WuOksONIM1M7NOz+/4yUasZCxHeP7PgwbgLtvqlp4hDE+Je+rvP0EUXICx+owCXZSDj1XEs7kAKJ5QGIQ+/ud1CglA1kBuIUXA38B8PJPgbd90/utNN0JvPp7YM0vgKatIC7XXXgHrvvyXUA0d92/tuYhAMAZ556F6b71YGrSSnwk9XkgBey9Lg7sesLb77kdjdh4qBOLV5yGs09aBsw8E4i61+3sm932Nn3iQsR9LKxntjfh/931MmbWVuCZG8/LO5cVX38Uma42/PZdk7F0HFzjuqMRHQ078b+vNKAUKXxkugPEyoB0D7Dqw8CCt2mv5VDCiLE7yPCZP7wOAPjee5YPcE8GNzbXJ7zPly6bctyPT7VzpQibiwkUZ+yGESvwoxhjvBhsOZTA0c4kVs2sNtZHHMrYf7QLJ88MJzRFCFvWRnINW7NUuzBth63VbBJ80cFER+MwNqQ69Cwm//J4gSJzpnJCKiarDoaORlT2NCDhVGDCVJ5JEna8mKCjN6uLZzLEdBEw3UKTyxWWRKpM48SL0hjE/ySGAi2uTamXEoNYso2EYUHR0pTBYUPnPsegDUDOAMl7ge6ziRruT20oZLSHeRdJnAQ6FKx7PWUlcNNe4LYVQE8rniz9Vzw6ZkPu9x+dBiTqMCsC/CVzBqZMngT4jPrOjl5saG1GPGJh1pT80lrbEo3ocNKorK1BpU8VN52xseFIAwBgzqKzgBXvc8+ToSxLKPREbjA55Uxld9RyVf57RtUiMgY2Av3U5hNwo/QAiZHsP9+cM0W/fcbwXKticP7oKkXJJeV3uPHpvweO/1kurwYWvB3Y/jfgm+Pxx5IT8KbINuC/8/ffv+QGnLfmNDhWFNdFeUek8XoteFuecflAYj3+cOAAvjV9CTBnJrtLGKeje88qkBy/CJhZ433f1tqNW1e7UvofueaSwH7DASPG7iDD7qZOAMDly6cOcE8GNy6+3S2r8a9vWzAgxz/nhPGhtp8uKVeiIKzgDyB48WtwvOTmv3z/RgDDU5gKALqT7qokbGSUUOz9M4FyXcPU2Q3rNDlxUnhGQVhnx7jKcDnsYQ12UR+EFGaKMIRJD8gzBjMp4DuuEv8d6cu1JVmOh3KmOjeQ0BkZvSp0BjhH/zaLK2WPHzJKE9iGKJGGS0WLeBMboLVTrz5LMInvEEylXgh07qZ5JG2oj0pIpilCLI++tfcUzkN1++geXyd4xkXldAgr9OTfR/QMlFcDN+1F19enoMLpwtv+vBTYcplLzzzqKq0t6PlfJBHHune8DWU+xsSu3c34zNYXUVtSgovf9da8Zj/5pb8hmbbxqzNOwcQTJnjfd3Ym8ZnXXYGuy9+VMx5MZXdEzANjLqr+XqvGn/9YlD89pUo/1hwvZzjnWKHn39Rvepz8z1XumdU/JLpyWIBZDK4kGkG3nTFSpE0q7/7UMYrCx2m7d/8vcOd5QOMm19AlnPvvwCkfASprsX/HEWTWvGR8H4YZ4zQvmZ5fndORq79NKIkOz0CDCSM82UGEF7LlJKZUlfUbrW44gHIiAeCG845PuSEVc8bJaMy0MComchomj5BQjKw9EE6BMhR868L2nhTW7GtBLGLh1DnhxZuGAl7d74pTcbUvJQgjlBRW7dmkfBtsOxxNP8wLnBDWUAubwz4xxPlKIVW0bmx3Iww6g5BDHtXw8Ebv452ZS7TXihNd6muMUxaOFG2cqVGL59RgAf5+c6VsCJK8VUnZljaKQAkEgEzGJRlvlYZnNCPoMx3LNL+LqNcCZwDZGBInDUX/PCG4AgZyU0ev8fcwpcVy0Xf5kpTYJDpjOwDLwrUT78GfM25OMbY8ADz0OffzR5/MqZXHLGU392+OnUX3WVrrNBf5D/5mWvPVC6KvZBhJnAxHOnLPqMRBQ8aqP3+femJioKWyffFHTBuz1UYk6QDc2CZRRirrGDb/mFgT3KX079eVUsoYxUrd0m9fPIyTe36MWT13A19tA869yc3L9cH03Ko/maLQ1AeTkJ86bugvU/WC6srjV3ljsGDE2B1EuPEPawEAX798SYEt39h4y3efAQC8e9W04yqT7j9StZBiTHlVxeTEFmOAFidqZa7jdixwfNbuZ3/vju8b3zow0fjjgc31bs79KbNqCmzJQ2Kg0ZAPO/YlKqPUYmfImrZhosbFYmzIF3TY0kYm0MJK6rQiWmyY52q0v7+OO1/cc8L/wEEEvYpwGfWnmFzpsFCNCcrxi6uLrOyfOrFAbgGolhrxg8agkbrpCfdoN/HlvxY2HE3HogW4iU7v1bU1KIeL8i+ZEjUqJM4AUxRLBS2o6Zqayrf4+6jLvQxDsT/Y0p13bAnIKOcEmbTHaU3iX1LX49krXwFWXed+ufBSYOrJ3jY6xsl8RqeDostSFglR2Dmj3nS9yJlojFg6ciq43xDKOIUdRtw4koxjatsvpEi32BRdpDRsSZSa65PpWtJ8wN0z/35UR9hRx2S8DM2oAsd9kIjPSdlBQO66hxGipN6a+hC2EsJwwKCmMVuW9Q0AlwOwATQC+JDjOPUD26v+w+Gs4ML5J04osOUbF/5c3VuuWjaAPZGBqEXFGJPSguX+Ka1YY1cVsOhrdPSm8cTWRgDA9efO7ddjDRQsKyeWkifaEgJh6rKGzemWOE9ocSItCUVOHOkL/Fh8UyZBEw5S8SbpcwbkFlOFkMguasPQwTnnRVvUdZro8o/Vepb9AZUmd7STz9WjNaGuNBOr7mtYkFGkyjZc84Zsjl7KIGxEu5sWjI6AJuoZBQKRIGMOsaAdifouGdYyNeb879Uczrx9BEY0kDtX1fHrCBb8KugcJgojpH6EmQdpKNeOmwgs+x5w6fcC2+j6zRmoNO6kxkMuqhgcr6Z3N+Xsmu6byPnB1JyWMAS4/GwT1TjYp9x3OaeR/nxJdE2SwuCPuEsUoulcuHGTL9yVZXuEyCMnB4wpuBHGOePR+0M8SxIHV19qPQwVDHbz/lbHcZY5jrMCwIMA/mOgO9RfoKLhM2oqhm1R574A5epeddLxjeoeK4pRTQ3jASQUe03CUM6KwTU/exEA8Ilz5g6p+1YsinU6SBTYacGiKxWjw+QxhV/aXl6i0KijRUltyHzaYsZ2WNqzdJyFaTcsNfqY0gNi5ThY5rIg1HFBjAndomVKEfn+OqgLR4oyq9T1QpEeziAwLczp/pm0C2h30zYS4zK3jXaTUJFUk/EjaYdEg0yRTq5mq+5YqkF6pkSpAAAgAElEQVQiyRculEqlE6jyyuuEEGUMGyH1Q5f7OY6hyJMBYxKD0h9Hfz2k7REjg0spMRmydH2ONcedE2eSOFa8fGB/FFXg1ODmBMl+nV7ZMf34p2vCPfuSZ5RLvfG/UzOeoSkfk9Tf2eP0zq4w7dmCe6oiLZhf+ju4MRgxqI1dx3ESvj8rUWwR0SGAHY1uHuqHz5o9wD0ZvHh2e5P3+bvvHji16tPmhKeohs2BBPqvHAqHYowPKTbXJ7A+W1LrpouGpzDV8QSV/FgyNZwoVFlJ4em+I0sdlY5XEsEIQ7MCgI7eVOGNjhMkzElafIV1Wh0TlTrd7eUIq1Tirl5zPdf+LFtHx1TzFD3asWaRxS3YJOqyEsPMFC3pq9xfUS1eQ04mISdQpd+GYDbi3f9Nec9JDR1aEv0rFNltSrhRdTXyT1Gt+RPk7BaJaJEOuvHBtUXPcZhaul1JKoukj9ZVC+cFkxCXyTlHhzaKOknoyIyhSXO4kWXRFXSCSoTWuD6ZVKMJNB6mGGqxk1HHsVtMzmaTGrPfSUXsnOJE0/T3SXfeHMPoUKI72568D8SI6UnrUylGaMyDEJZlfQvAP8Et0XzeAHen33HVydMGuguDFv9018sAgBsvGJicT8rDenH30QJbBtGX+YP9gTDUmrCgaPz337NiWEd1idJ2ybLJBbY8NpDAR08qXB64KT+SQLTQglKsWdBCqVxgSAM5w3LZ1LGyAxwHzJ1QmNVAokthacPFKKqjNyvAd8an0X1YEUnJoj3rLFAjShJho2OFLp/UX5akLxBG7EliyJqGP0VSTfNTIWPePRYKbkPPjGV4yEjYR0cJB2RG/IGjbq1X1UiKGhbwEuMHyAkhVZTm99HL6Q5hJJBgkvoe4ui+KnRjnes/GTBqn00gIaxRBl0CqTEicap4RpxvzUDMP5ONLlGnJgOV28TksOzN3tMZjACmSVSRrp3/dE31gAmSEl4mo9nUtsRIB3LzWZglS0pVcGag9s1E+6c5IgyLqzs7TxUj0NgfFQwGCwb8zCzLetyyrI3Mv8sBwHGcLzqOMx3A3QA+pWnjY5ZlrbEsa01TUxO3yaCGX8RnsBtFgwH/fP7AKDAfCllb149iauaGxehBOHa+/sBmAO4i5oqVb4xyWn1JIeVAL+sTQuYFSxwN+5rd0mdzhLT2vdntTWUr/CBvc5iyJP2Flixlu9sgJkRoyjoYJoe8tyZqohYHXMo/SkZ5hqVKe6RFlRpdpX4WI4gnhU4Ehr4vtuxW4DhhclsF0VaTgUHnUmMQn6LxYor0SI5FmKxErfy6DiRwN6Mm9xyePX9cnt6BRCCIxh8ZMjZjjL9z5dS8551o6oWiP17OrnLNiPIaRlmZoEa3TLnYBB2F2HSfTPdZBRmRJvaT1Imb8ZxR+mtDxs90Ju/bFLGsb3UdlabyWSaHlMlBNjZ73JlMn8YYHIBEZfePpbPnjwMgzRvXblKwzrMO5Dgo5BCkd9S0EGUjD2fZDqaoqgoTVZkcR2HSouiZMd0XHcIwHoYaBnx17DjOBcJNfwvgIQBfYdq4E8CdALBq1aohR3V+bX8rAODKk94YxsCx4NtXLRuw6CB5n7962aLQ+/Znn2lS/v/s3Xd4VGX6//H3M5NJTyAEQg9dOqIiSxEURIoFYVdF1xXb6u7aWAu2XcVVv67+7Ou6rl3WdbGh2HURXSsoRUQi0kSkh17TZub5/TEzIWVIJmGSM5N8XteVK5kz55znnpkzJ+c+T0uqRT+kuvbT9kDNwvybI/2ax79Jx+bW6f5La2tqMd1PdUIVKZFOpRESad/U2ibqdSF0QdI9gsHjQhdtNf2OHdb3vvdESn4MNP8/VP/YirVnoZFGw40cGy2pwZtqFS/OIhlBuCYia8ZcfXLZJSedD5flV3nBWBy8OZAWQY1fVd+NSEZjDZVVNgH47rbR5R7/4fgunN6/TbkL7ecv/kW5/UQyim6orFCNaeg4Lilzs+n+M48slyCFPsecCEdjrlgbG0ombA16nbVtmsKGXQWH7B9+RBXHc8X3OlQzvb+ocsLxq6PbMXPR+hp1EQolD+ES1HF9WvHe0s2VlocS5HDlL93wfbmRrgd2bMbXPx1sLdY0NZGHz+7PkC7NK21fVWVI6CW1r2JApdDx0L7McdW7TSZ5G/eUO1d1aZHG8CNalD4+tV9rPG7DSb1aHXLfVZVX9v/DnRP68OuBuVW2kilNdqu44RJap2yt53Fdm/P5qm1VxhR6ldXVfB7frQXvLNlErxpMI1hcmyb8we9xuM+2pPQ8Xz+DWtXFNUWscDzZrYoxppu1dmXw4XjgByfjqSuhL+1vBnVwOJLYd9ax7R0rO3THui4795fUosYr9M89kikHnHDVid1oUosBuuJVpLWiIYe6MDqk4P+jaNWglRW6CKyqyV5ZoeQ1LcJWBZuCtQ+RTjFSk6lIILL+tyGhacEiuZAIJUpVXUgejnLzY/c/F76dAanNSfUEmjRXvON+cOTj8u/7wE7N6Nk6kxvG9qhUxtQx3RnRvfJI/z1aZTCgY1bEsd44tgdNUjz0alO+z3hNLrKeu/BY8vdUnqe1bA12aJTfSPoEVlynbEuaK0Z0pVfrzCqnA+uak85P2w9UugAvewM6dFOxbHJ3zsD25ebLPfOY9ny2chsndD+YKFR0+ciuLHpufrn3r2K5LpeptkYp3M2AHq0yGFRmDvOJR7flX3PXliYEoddQ9hzlchkSy+yj2Fc5GQ/nxUsH8cbiDZXmHS7xBuI61Aji4QaOev2yIfy4bX+l5bnNUrnqxG6cGaZ7128G5fLud5UTzQNVTPt17xn9uOuXNZvaMdRq5cj2lUeof+Cs/tx6WuV5pUPjKlT8n3zBkI786ph25WrdHvvN0ZXmLD69f/iKj6punoVac1R1Qy47+N53LXPz4D+XDCI/WCMZMufaEyqVO7ZP+e45140+gufnrS237OjcpozscfAc0yF4DJSd6qhpaiJDulZO5Mu2TAvdgKzqmqZfuyb8olOzclMZPnvhsaUJZ8jgztnlvsdF3srf43DOHNCOcX1bHTIp7xlmHtshXbJ5eM5Kjq/i+19R6ybJDO2azRUjuoV9buveoholoaXveZvK8Y0/sk3YmwGh5uaRtKSIVzGd7AJ3G2O6E5h6aC3we4fjqVP9Ipzuo7GaffVwR8sP/ZNuHWGTzZCa9LcI9dH68yk9I94mdHH5i041GzgrdFFT1xXl1zTgeXXDqenAYqHEpeyd9Kr8cVQ37nnvB3pHeL54cNKRtEiPrKb2ttN688Snq8ms4by5kTbTD32Hyl6QR+K4MBdH4YQu/H8bwUB/oXkUI0nUrzyxG01SPGFrW8J5+Oz+pRcQ1ZlxyaDyA7Gc/iic9jC4PVw7OpHEBFelZHRcn1Z8vmorFw7tWG55RrKH96YMC1vO5SPCd/9456phYfvwvXjpoNIBfcrq1SaTR845qtLyk3q14qX56yqNO3Hbab1KW3iEnFAh6Xa5DA9N6s+xZc5hN47rQU5GEn0rHOddcw5eqI/r05ovVm0vN9XPvy4aWK7WJiPZEzZ5KNu64MFJ/fluw+5y/RZ/uvuUCq+jN9PeXFruxsRff1l++ru+7Zrw8XUnVCqrrOOPaMGqu06ucp1I9GidwYK1O8vVRr971bBy559pp/XmhrE9Si/sQ/17q+pLftfEvtzz/g/0LFOjdfcv+1bqm9mnbZOwU5SdOaAdLy1YV9pctax//ubock2zQ3Iyk8kJU9NmjDnk/487J/Tlzgl9Ky0PxXTZiMpT3LlchiRX+P/Hkwa0p2WY92Vo12xev2xIpeMQAv1VUxIrXw+EzodXjiz/nTPGVGpemp2eRHY1A0S+feVxzP+p/FghFw7tyC86HTyPTh7ckc9WbqNPmSTn+CNalLthOKJ7Ds9fPLDceaxJiqdWswdcMbIbV4wsn6C9dtnQco/H9mnFO1cdVy7ZDeeNy4eW6yJyzsBcurfMKHeD6tkLjy3Xyz0j2cNLvxtcbj8et6vSTZoZlw4q9/j20/vwl7fyyg3C+PdfH8Wq4CCxIcaYQya6My4ZFHaO8190zubHu04Oew3wyu8Hlw70VVayx80Lvx1UaTnAX3/Zl9cXbSA7zP/XJimesO/rST1bMuvyoWGP1wfOOpLCMF1cQjcqw23TUJhIBgCIJwMGDLALFixwOowambNsCwUlPk7t18bpUGLOviIvfaZ9QHpSAkv/MsbRWDbtLuCl+eu4YkTXiEc7/erH7WQkV64FqY61NuImkEVeH09/voZzjs2tNOdhVXbsL+boO2bzu+GduenkyJPrSLyzZBOX/2cRC/88qtp/5A3FGY99ydg+rfjtsM413vaLVds4OjerysE+YtEPm/ewbNMeJh4V2cB6BcU+3vluE+OPbBPxoGgLftpB7zZNIn5vDhR7SU5wV3vTYdmmPZz6yOd8MvWEGvXLEucs+nknORlJpZ+XtZbdBSU0rUEfTIDVW/fRIiOpVv3aamrBTztomZkctg/m4dh9oIT/rchn/JFtIv5fYa1lxtfrOKVf61pPjybxw+8PNCaPVtcCiR3b9hWR4nFH3KqqKtZaHvpwJecOyq1xFyanGWMWWmsHVLuekl2RxmtvYQnpSQkNepRkEREREWlYIk12Y70Zs4jUoZpOpSIiIiIiEi8a7jjTIiIiIiIi0mgp2RUREREREZEGR8muiIiIiIiINDhKdkVERERERKTBUbIrIiIiIiIiDY6SXREREREREWlwGtw8u8aYrcBap+OoRnNgm9NBSIOj40rqio4tqQs6rqQu6LiSuqJjK7Z0sNa2qG6lBpfsxgNjzIJIJkEWqQkdV1JXdGxJXdBxJXVBx5XUFR1b8UnNmEVERERERKTBUbIrIiIiIiIiDY6SXWc84XQA0iDpuJK6omNL6oKOK6kLOq6krujYikPqsysiIiIiIiINjmp2RUREREREpMFRslvPjDFjjTHLjTGrjDE3Oh2PxDZjTHtjzMfGmGXGmDxjzJTg8mbGmNnGmJXB31nB5cYY87fg8bXEGHN0mX2dH1x/pTHmfKdek8QOY4zbGPONMebt4ONOxpivgsfIS8aYxODypODjVcHnO5bZx03B5cuNMWOceSUSK4wxTY0xrxpjfgietwbrfCWHyxhzdfB/4FJjzAxjTLLOV1IbxphnjDH5xpilZZZF7RxljDnGGPNdcJu/GWNM/b5CqUjJbj0yxriBR4FxQC/gHGNML2ejkhjnBa611vYEBgGXB4+ZG4E51tpuwJzgYwgcW92CP5cCj0HgRA5MA34BDASmhU7m0qhNAZaVeXwP8GDwuNoJXBxcfjGw01rbFXgwuB7BY/FsoDcwFvhH8DwnjdfDwPvW2h7AkQSOL52vpNaMMW2Bq4AB1to+gJvAeUfnK6mN5wh8/mVF8xz1WHDd0HYVy5J6pmS3fg0EVllrf7TWFgMvAqc7HJPEMGvtJmvtouDfewlcOLYlcNxMD642HZgQ/Pt04F82YB7Q1BjTGhgDzLbW7rDW7gRmoxNwo2aMaQecAjwVfGyAkcCrwVUqHleh4+1V4MTg+qcDL1pri6y1a4BVBM5z0ggZYzKB4cDTANbaYmvtLnS+ksOXAKQYYxKAVGATOl9JLVhrPwV2VFgclXNU8LlMa+1cGxgU6V9l9iUOUbJbv9oC68o8Xh9cJlKtYFOso4CvgJbW2k0QSIiBnOBqhzrGdOxJRQ8B1wP+4ONsYJe11ht8XPYYKT1+gs/vDq6v40rK6gxsBZ4NNo9/yhiThs5XchistRuA+4CfCSS5u4GF6Hwl0ROtc1Tb4N8Vl4uDlOzWr3Dt9jUctlTLGJMOzAT+aK3dU9WqYZbZKpZLI2SMORXIt9YuLLs4zKq2mud0XElZCcDRwGPW2qOA/RxsDhiOjiupVrB56OlAJ6ANkEageWlFOl9JtNX0WNIxFoOU7Nav9UD7Mo/bARsdikXihDHGQyDRfcFa+1pw8ZZgcxmCv/ODyw91jOnYk7KGAuONMT8R6E4xkkBNb9NgM0Eof4yUHj/B55sQaAam40rKWg+st9Z+FXz8KoHkV+crORyjgDXW2q3W2hLgNWAIOl9J9ETrHLU++HfF5eIgJbv1az7QLTiCYCKBgRLedDgmiWHBfkZPA8ustQ+UeepNIDT63/nAG2WWTw6OIDgI2B1skvMBMNoYkxW8Sz46uEwaIWvtTdbadtbajgTOQx9Za88FPgbOCK5W8bgKHW9nBNe3weVnB0c/7URgMI6v6+llSIyx1m4G1hljugcXnQh8j85Xcnh+BgYZY1KD/xNDx5XOVxItUTlHBZ/ba4wZFDxWJ5fZlzgkofpVJFqstV5jzBUEviRu4BlrbZ7DYUlsGwqcB3xnjFkcXHYzcDfwsjHmYgIXAmcGn3sXOJnAwBsHgAsBrLU7jDF3ELjhAnC7tbbiAA0iNwAvGmPuBL4hONBQ8PfzxphVBGpIzgaw1uYZY14mcOHpBS631vrqP2yJIVcCLwRv6P5I4BzkQucrqSVr7VfGmFeBRQTOM98ATwDvoPOV1JAxZgZwAtDcGLOewKjK0bym+gOBEZ9TgPeCP+IgE7jZJSIiIiIiItJwqBmziIiIiIiINDhKdkVERERERKTBUbIrIiIiIiIiDY6SXREREREREWlwlOyKiIiIiIhIg6NkV0RERERERBocJbsiIiIiIiLS4CjZFRERERERkQZHya6IiIiIiIg0OEp2RUREREREpMFRsisiIiIiIiINjpJdERERERERaXCU7IqIiIiIiEiDo2RXREREREREGhwluyIiIiIiItLgKNkVERERERGRBkfJroiIiIiIiDQ4SnZFRERERESkwYmZZNcYk2yM+doY860xJs8Y85fg8k7GmK+MMSuNMS8ZYxKdjlVERERERERiW8wku0ARMNJaeyTQHxhrjBkE3AM8aK3tBuwELnYwRhEREREREYkDMZPs2oB9wYee4I8FRgKvBpdPByY4EJ6IiIiIiIjEkZhJdgGMMW5jzGIgH5gNrAZ2WWu9wVXWA22dik9ERERERETiQ4LTAZRlrfUB/Y0xTYHXgZ7hVqu4wBhzKXApQFpa2jE9evSo0zgPW/EB2Lacje42tGnZ0uloJFLWwqbFFJJEYuueuIyJbLuN3wCwM7ENWc3Lf962cC9mx6oah7KnaS8yU5NqvJ04KHgc7Gram6apGnpAREREpLYWLly4zVrborr1YirZDbHW7jLG/A8YBDQ1xiQEa3fbARvDrP8E8ATAgAED7IIFC+oz3JpbvwCeOpHbMq7ltmv/6HQ0EqkvHobZq3jfcxJj//Rq9esH+X+ej+uZUcAell04n54dWgFwYMYFpC5/HUjn56QjKJj8Lsvef5IJ6+4p3fbjzAn0uOBvtG7WBICCx0aQsmURL/m7Mun21ysXtmkJPD6M3R3H0uSClw7n1UqUee9swxdFXci85E2Oys1yOhwRERGRuGWMWRvJejHTjNkY0yJYo4sxJgUYBSwDPgbOCK52PvCGMxFGn61cSS2xqmAnzL4VgE9bTa7Rpq7cY9nXYRQAPZ/tztzlG1i/bVcw0YUvTniR3Jvm071tCyZcfDPctptdU7ew87otjLhmemmiC5By8dsATHJ9xNyVWyqVVfL5IwA0+el9tu/eV+l5cY4XNz/a1iQluJ0ORURERKRRiJlkF2gNfGyMWQLMB2Zba98GbgCuMcasArKBpx2MURqrH94F4BXvcH499vgab55+3n9K/x48oxft/t4BgK/MkQw9YVyl9ZumJZOVnlx5R4lpFDfvDcDcV+6v/Pyyg/eCXlsY0Q0vqSehRu+tmoT5XEVEREQk6mKmGbO1dglwVJjlPwID6z+iumdVsRs/rA+AB7xnMrdtk2pWDiMhCW7dyfb/3kPBj/NI9O5lb2ouR0/+Z413lXjhW3BvZ84sfI0i718P1hTuXIvHX1S63tb5M2Fk75rHKnUi9HVPTIile4wiIiIiDVfMJLsiMW3zUgB6tMqo/T5cLrLH3lT6MKe2+0nLxo+L9q6tPDl7AZeM+wUARV/8gyRgVtoZTNj/Ksfs/6z2sUrU+f2BdNfjjnBgMxERERE5LKpiEIlAydqvAOjdpb3DkQT4j70EgEu+Gs03i+bD+oUkLQjUEu8/6ncAtGIbB4q9h9yH1LNgjqs+uyIiIiL1Q8muSHVKCvFsWcxCfzdyW8XGVFEJo26lqFlgiq3cN34JT40E4JaSCzhn5LGsazaYdAr43/KtToYpZfj8ltREJboiIiIi9UXNmB2Uv7eo+pXEeXsDs11ttll0bZnucDBBSekkXfYZ3NmCbLMHgCLr4aKr78LlMrRJtbhdm3h33tvQ9xKHgxW8RWTYfRT7/E5HIiISM/bs2UN+fj4lJSVOhyIiMcLj8ZCTk0NmZmZU9qdk10HquxdfPvQdw5UpHqfDOCghkZIzpuN59XwAZgz7gAuapwHgPm4KvDiP9A2fAkp2HbfmUwCapyc6HIiISGzYs2cPW7ZsoW3btqSkpGCMrolEGjtrLQUFBWzYsAEgKgmvkl0HJbjUijwurJwNgMXQPCPJ4WDK8/SZQFHLxdikTC7IzD74RI+TKcKDH108xARfMQCfp4+m8kRTIiKNT35+Pm3btiU1NdXpUEQkRhhjSE1NpW3btmzcuFHJrki92B/o9/qpvx/pibH3lUlq0Sn8ckoYY7+o52ikKjsPaMAwERGAkpISUlJSnA5DRGJQSkpK1Lo3qGrRQRZNtBsvfLggrTkuV3zVlKZRyO4C9YWKFV1zYqTPt4hIDFDTZREJJ5rnBiW7IhEwQGGJz+kwamR5+zPx4mLt9v1OhyJBCXF2s0REREQkninZdZBVxW5c6dk6OqPC1Zf0pECT6/1F8ZWkN2Qu1WKIiIiI1BsluyIRsFj8cXZ3wuMOfL1Xbd3ncCRig8eOcl0RERGR+qNkVyQihkR3fH1d0hLdtDB7aFK0xelQGj2vP5Dsxtn9EhEREZG4Fl9X7yJOWPcVbvzkNouv6RFc2V0A2L32W4cjEa/PD0DbLI08KiLSUA0fPpwTTjih0vIVK1bg8Xh46qmn6j8okUZOya6DVMsTJzZ/B8Rff8uUrscBaDTmGLCnMDDlULHX73AkIiJSV/r160deXl6l5TfccAPdu3fnwgsvdCAqkcZNya5INaw7iZm+4yjxx2eiEm99jRsis30lAC2bJDsciYiI1JW+ffuybds28vPzS5d9+eWXzJo1i3vvvRe32+1gdCKNU4LTATRuSkLignFRYhPolJ3mdCS1UlASn0l6Q5KyYS4ArrQWDkciIhK7/vJWHt9v3ONoDL3aZDLttN612rZv374A5OXlkZOTA8DUqVMZNWoU48aNi1qMIhI5Jbsi1QrclEiIswGqQjbuKnA6hEbP60pktb81vozWTociIiJ1pG/fvhhjyMvLY8SIEcycOZN58+axcOFCp0MTabSU7DpI9brxIfQ5Jbjiq89uyNFFXzsdQqNXWOKjGI/TYYiIxLTa1qjGioyMDDp06EBeXh5er5ebb76Z8847j/79+zsdmkijpWTXQUp240Ooq+7ewjgb6Kl5NwCyjbNNwgQMgRslbZtqNGYRkYasb9++5OXl8fjjj7Nu3TrmzJnjdEgijZqSXZFqBW5LdMlJdziOGkpuwo6UjrDPUFDsIyVRA2M4JTRImCdOm8KLiEhk+vXrx6OPPsry5cu55ppraNeundMhiTRquvJykqp240LoY3LHaTNmgPy9hU6H0KgVFewHbNw2hRcRkcj07duXXbt2AYEph0TEWUp2RSLkjrN5dgES3XCqex7rd2qQKsd4i+i0ex5JlNAyU1MPiYg0ZJMmTcJay5YtW8jIyHA6HJFGT8mug1SxG19ccVgr57GBfsY/btzqcCSNmDdQq77YdsXjjr9jSERERCRexUSya4xpb4z52BizzBiTZ4yZElx+mzFmgzFmcfDnZKdjlcYn2N0yLmt2/QMuBuDL1dsdjkTy/B0xcXgMiYiIiMSrWBmgygtca61dZIzJABYaY2YHn3vQWnufg7HVGauq3bgQGlzIF4cfWIoncD/r4xVbsdYq2XJQVlqi0yGIiIiINCoxkexaazcBm4J/7zXGLAPaOhuVSHltmsTvtDHTPXfz9R0PkZTgosTnJ9HtwgIpHjeWQEJvLVhr8RP4faDIR2KCC5crMHGOyxiKvX7SkhJwGSjpfCJ9fnWTsy8sjsRhK3gRERGRuBYTzZjLMsZ0BI4CvgouusIYs8QY84wxJusQ21xqjFlgjFmwdWs89U2Mv5rCxswVc9+WCHQchs0dTOcsD809RbhL9pFmC/AV7sEU76XkwG68BXuwhXsxxXtJ8O4jybufFP8BshIKSfLvJ50CUu0BTPFe0jiAv3APLfd9T3reC06/urhSWOJzOgQRERGRRiUmanZDjDHpwEzgj9baPcaYx4A7CGSFdwD3AxdV3M5a+wTwBMCAAQPiJoOMm0AFCNRsxp12AzAXvU8LoEUUd7vovtNodmBNFPfYgG1fDaCRmEVERETqWczUVRljPAQS3Resta8BWGu3WGt91lo/8CQw0MkYpXEKddWNy2RXnPfzPADWqGeGiIiISL2KiWTXBEbNeRpYZq19oMzy1mVWmwgsre/Y6pSqduOKOya+LbFCiX/EgjdJPB10r05ERESkPsVKM+ahwHnAd8aYxcFlNwPnGGP6E0gLfwJ+50x4ImgkY6kVay0GSExwOx2KiIiISKMSE8mutfZzwlcVvVvfsdQnVezGB58/8EnF4zy74ryCEh+pQGGx1+lQRERERBoVNcwUqUYox01Pjol7QxJnQjdLuuRkOByJiIiISOOiZNdB1qpuNx6EPqVkj5qhSs0dKA5MOeRxq2WAiIiISH1SsisSIZdyFamFJt89C4AnQS0DRETk0K688kpOO+20csueeeYZunXrRmJiIk2bNnUossjNnTuXs+9k4dAAACAASURBVM8+m3bt2pGYmEhmZibHHnsst9xyC5s2bSpd77nnnsMYU/qTkZHBkUceyd///ne83vLdfmbNmsXw4cPJyckhJSWFDh06MGHCBN5///36fnlRF2+f7+F68MEH6devH36/v97KVLIrUp1g1a7RCMSl9E5ErtiVAkBOixyHIxERkVi1evVqHn/8caZNm1a6bOPGjVx66aUMGTKEjz76iA8//NDBCKt3//33M3ToULZu3cqdd97Jhx9+yIsvvsiYMWN44oknuOiiiypt88orrzB37lxmzpzJwIEDufLKK7n99ttLn//b3/7GxIkT6datG08//TTvvPMOf/7znwH46KOP6u211YV4+3yj4fe//z35+flMnz693spUVYNIhDQ+ldSGz8Js3zG09OjeooiIhPfQQw9x5JFHMmDAgNJlK1euxOfzcf7553PcccdVuX1RURFJSUl1HeYhffzxx0ydOpUpU6bw4IMPlnvu5JNP5qabbuKVV16ptF3//v3p2rUrAKNHj2bVqlU89NBDpQnvfffdx4QJE3j66adLtxk5ciSXXHJJvdYORqKmn0FNPt+6KN8JKSkpTJ48mfvuu48LL7ywXsrU1ZeD1GU3vriU7cphaJ4e2/+ARETk8AwfPpwTTjih0vIVK1bg8Xh46qmnwm5XVFTEv//9b37961+XLrvgggtK93XiiSdijOGCCy4A4LbbbsMYw9KlSxkzZgzp6emcddZZpdu+//77DB48mJSUFJo0acKECRNYvnx56fOh7X/44QfGjBlDWloaubm5PPtsoNvN888/T48ePUhPT2fEiBGsXr262td+zz330Lx5c+65556wz6elpZXGX5Vjjz2WvXv3kp+fD8COHTto1apV2HVdrqrTmFWrVnHeeefRqVMnUlJS6Ny5M3/4wx/YuXNnufVWrFjBxIkTycnJITk5mdzcXM4888xKzanLqu4z+Pbbbxk/fjxZWVmkpKQwdOhQPvvss9Lnq/p8I9n+cMsvu4+VK1dyyimnkJ6eTocOHbj99tsr3Uj49ttvmThxItnZ2aSkpNC9e3f++te/VlqnujIBzj77bL7//nu+/PLLQ76/0aRk10HFPj9+vzLeeKFctzzdrIlM6H1K0ABVIiINWr9+/cjLy6u0/IYbbqB79+6HrMmaN28eu3btYtiwYaXLbrnlFv72t78B8OijjzJ37lxuueWWctudfvrpHH/88bz55ptcffXVQCDRDSUuL730Eo899hhLly7luOOOY8OGDeW2P/PMMznllFOYNWsWxxxzDBdddBE333wzjz32GHfffTfPPvssy5cvL5eEh+P1evnkk0846aSTSExMrP6NqsKaNWtwu92kp6cDMHDgQKZPn869997LihUrarSvjRs30q5dOx566CE++OADbr31VubMmcPJJ59cbr1TTz2VDRs28Nhjj/HBBx9w9913k5SUFFHNcbjPYNGiRQwZMoQdO3bw5JNPMnPmTLKzsxk1ahQLFy4Eqv58I9n+cMsva+LEiYwcOZJZs2YxYcIEpk2bVq6Z8ddff83gwYNZvXo1Dz74IO+88w7XXHMN69evL12nJmX279+fzMzMeutzrWbMDivy+klJ1Ci/8UA1u2XorYhYoTcwGnNSgr7nIiJVeu9G2PydszG06gvj7q7Vpn379uXRRx8lPz+fnJzAOA1ffvkls2bN4t1338XtDv9/YN68eRhj6NevX+myLl260LNnTwB69erFoEGDKm131VVXMWXKlHLL/vznP9O5c2fee+89EoIDIw4ePJgjjjiC+++/nwceeKB03alTpzJ58mQABgwYwFtvvcXjjz/OmjVryMzMBGDTpk1MmTKFtWvX0qFDh7Dxb9++ncLCQnJzcys9V7F2NKHCYI0+nw+v18vevXt5+eWXee211zjttNNITU0F4J///CdnnHEG119/Pddffz3Z2dmcdNJJXHjhhYwePTpsPCHDhw9n+PDhpY+HDBlC165dGTZsGN988w1HHXUU27ZtY+XKlbzxxhuMHz++dN3qEvyQcJ/B1KlTyc3N5aOPPipN/seMGUOfPn244447mDVrVpWfbyTbH275ZV177bWlN2JGjRrFRx99xIwZM0qXXXfddWRnZzNv3rzSz2XkyJG1LtPlctGvXz/mzZsX0Xt8uFSz6zCLqsdiXegT0mjMUhuhmyRNUjwORyIiInWpb9++AOVqd6dOncqoUaMYN27cIbfbuHEjmZmZNa4VnThxYrnH+/fvZ9GiRUyaNKlcUtmpUyeGDh3KJ598Um79sjFlZWWRk5PDoEGDShNdgB49egCwbt26Q8ZxqKk0N2/ejMfjKfdTMfnt0aMHHo+HZs2acdlll3HuuefyzDPPlD5/xBFH8M033/DJJ5/wpz/9if79+/P6668zZswY7rzzzkPGBFBcXMxdd91Fjx49SElJwePxlNaeh5p1Z2dn07lzZ2688UaefPJJVq5cWeU+K6r4GRQUFPDJJ59w5pln4nK58Hq9eL1erLWMGjWKTz/9tMr91XT7aJR/yimnlHvcp08ffv75ZwAOHDjAF198wbnnnlua6B5uzAAtWrRg48aNVb4X0aKaXYepKWj8MKrZLaV3InIFxT7culMiIlK9Wtaoxoq+fftijCEvL48RI0Ywc+ZM5s2bF7bpaFmFhYW1GliodevW5R7v3LkTa22l5QCtWrVi7dq15ZZlZWWVe5yYmBh2WSjGQ2nevDnJycmlCVLZ5fPnzwfgiSee4Mknn6y07euvv067du3IyMigQ4cOJCcnV1rH7XaXq6XduHEjY8eO5S9/+QuXX355pZhDbrrpJh555BFuvfVWhgwZQkZGBuvXr+eXv/xl6esxxjB79mxuu+02brrpJrZv306nTp2YOnUqf/jDHw75mkMqvtc7duzA5/Nxxx13cMcdd4Tdxu/3H7K/caTbR7P8Zs2alXs+KSmp9P3ZuXMnfr+fdu3ahd1XbctMSUmhoKDgkPuMJiW7DlOuG/t8MTban8SRor108q5mDcc4HYmIiNSxUMKWl5eH1+vl5ptv5rzzzqN///5VbpednV1p0KRIVLwJn5WVhTGGzZs3V1p38+bNZGdn17iMSCQkJDB8+HBmz55NcXFxaYKckJBQOrr022+/HXbbPn36lI7GHKk2bdrw29/+lilTprBy5UoGDhwYdr0XX3yRyZMnl05VBLBv375K63Xu3Jl//etfWGv59ttv+fvf/85ll11Gx44dq6yRh8qfQdOmTXG5XFx++eWlTcQrqmpgrZpuH+3yK8rKysLlclXq7324Ze7YsYPmzZtHHMfhULLrsEM1/ZDYoRpdqbUdawDwJtfNBYaIiMSWvn37kpeXx+OPP866deuYM2dOtdv06NGDkpIS1q9fX2UNWnXS0tI45phjeOWVV7jttttK+wivXbuWL7/8kiuvvLLW+67O9ddfz0knncQNN9xQaeqhw7Fu3Trat29fafkPP/wAcMiRmiHQBNfjKd+FKDTidDjGGPr3788DDzzA008/zdKlS6tNditKS0tj2LBhfPvttxx99NE1SixjYfuKUlNTOe644/j3v//NrbfeSkpKSlTKXLNmzSFvUkSbkl2HKdWND8keDS4ktTc/cSBVD6MhIiINQb9+/Xj00UdZvnw511xzTUTJa6h57tdff31YyS7AHXfcwSmnnMKpp57KZZddxr59+5g2bRpNmjTh2muvPax9V+XEE0/k7rvv5sYbb2TJkiVMnjyZTp06UVhYyIoVK3jxxRdJS0urcQVCnz59GDFiBBMnTqRTp07s2bOHd999l3/+85+cddZZYQfFChk7dizTp0+nb9++dO3alddee63SdDdLlixhypQpTJo0ia5du+Lz+XjuuedISEioNAhTpB544AGGDx/OmDFjuPjii2ndujXbtm1j0aJF+Hw+7r676ub6Tm9f0X333cfxxx/P4MGDufbaa2nXrh0//vgjixcv5pFHHqlxmbt27WLFihVcd911NYqjtjRAlcNUsRsHrKYdksOjLrsiIo1D37592bVrFxCYcigSHTt2ZODAgbz11luHXf7YsWN555132LVrF2eddRa///3v6dmzJ59//jlt2rQ57P1X5frrr+ezzz4jOzubm2++mVGjRnHGGWcwffp0Jk2axMqVKw85IvWh3HPPPfj9fm699VZGjx7NpEmTmDt3LnfffTfPP/98lds+8sgjjB8/nj/96U9MmjSJvXv3MmPGjHLrtGrVitzcXB544AHGjx/POeecw8aNG3n77bc55pjadUE6+uijmT9/PtnZ2Vx11VWMHj2aKVOm8N1335UbHTpWt6/o2GOP5YsvvqB9+/ZceeWVnHzyydx7773lbszUpMx33nmHxMTESoNr1RXT0JrRDhgwwC5YsMDpMKq2fgE8dSIXFF/Pw3++niapGqU1lu26syuf+vsx/tbXnA4lZiy+fzxN9v1Ip2lLnQ4ltm1aAo8P4+l2d3Lxb+uu+ZiISLxZtmxZ6dQrAs899xxTpkxh06ZNhxz1VqQhGDduHM2bN6/2ZkV15whjzEJr7YDqylPNrsM09ZBIA7ZvCxAYkVlERORQzjvvPNq2bcs//vEPp0MRqTOLFy/m448/Ztq0afVWppJdhzWwivUGS4NUSW34vn0ZgJzWh+5TJCIi4na7eeaZZ1SrKw3a5s2befbZZ2s8Avfh0ABVDlOuK/HIaKbdiLiXBpLdjRl9HI5ERERi3aBBgxg0aJDTYYjUmbFjx9Z7mUp2HdbQ+kw3VErtpDb8CSnML+5A5xbpTociIiIi0uioGbPDlOrGA6tstyK9HxHxmQTy/B2dDkNERESkUVKy6zBV7MYH5XaV6dCtnj/4Be+UneZwJCIiIiKNj5Jdh2k05tinT0gOV24zDTgiIlKRunKJSDjRPDfETLJrjGlvjPnYGLPMGJNnjJkSXN7MGDPbGLMy+DvL6VijSuf52KfPSGrJ5w8cPKlJbocjERGJLQkJCXi9XqfDEJEY5PV6SUiIztBSMZPsAl7gWmttT2AQcLkxphdwIzDHWtsNmBN83GAoj4p9Xr/Frw+qHDXrjkxo1GqPO5ZOtSIizktOTmbfvn1OhyEiMWjv3r0kJydHZV8xcwVmrd1krV0U/HsvsAxoC5wOTA+uNh2Y4EyEdUMteGKfS3PsSi35rSVNtboiIpW0aNGCrVu3cuDAATVnFhEg0Hz5wIEDbNu2jRYtWkRlnzE59ZAxpiNwFPAV0NJauwkCCbExJsfB0KLOrxN8XEhNVMIitVPs9TsdgohIzElOTqZly5Zs3ryZoqIip8MRkRiRlJREy5Yto1azG3PJrjEmHZgJ/NFau8dEUKtmjLkUuBQgNze3bgOMsh37i2nTNMXpMKQaqtsNRzdqIpGdluR0CCIiMalJkyY0adLE6TBEpAGLmWbMAMYYD4FE9wVr7WvBxVuMMa2Dz7cG8ituZ619wlo7wFo7IFpV3iLlKNuVWvD5LS6XDh4RERERJ8RMsmsCVbhPA8ustQ+UeepN4Pzg3+cDb9R3bHVJrZjjgUXZbhg6dqu2aQmZ5gDekhKnIxERERFplGKpGfNQ4DzgO2PM4uCym4G7gZeNMRcDPwNnOhRfndA8u/FBqW55GrMrArt+DvzKGehwICIiIiKNU8wku9bazzl0TnFifcZSnzSljUjDtjOlvdMhiIiIiDRKMdOMubHScPvxQRWZlXl1pyYibh08IiIiIo5QsuswpQuxz4Ky3QqKNJ1OtXy71gOwv9jncCQiIiIijZOSXYepZlfiUUZSzPSAiFne798GoG3rNg5HIiIiItI46YrVYcp144MqdivQG1KtIlcK220zfJntnA5FREREpFFSza7D1O0x9qX59yu5kxorLPGxy2bQPivF6VBEREREGiUluw5TM+YYt/k7Uigk0V/sdCQxRbl/9UI3stLU5FtERETEEUp2HaZUN8bt3wbAN2lDHQ4k1ijdrU5BcGCqnIwkhyMRERERaZyU7DrMr5rduLDb3czpECQebF8Nea+DtdjgraxmaYkOByUiIiLSOKl9ncO27i1yOgSJgFFFZljWWozenINePBe2LoPL57O30IsHSFczZhERERFHRL1m1xhznzGmd7T321C5lCjECX1O4ahhQgVblwV++4pxuQLHjG4GiIiIiDijLpox/wA8YYz5yhjze2NMkzooo8FQM+b4sL+oxOkQYorSt2p8cBO5B74nKUHvlIiIiIhTop7sWmufstYOBSYDHYElxpj/GGNGRLushsCnuYfiQlaaBhkKR0dvBR2CA5mt+ZQmvh00tbudjUdERESkEauTAaqMMW6gR/BnG/AtcI0x5sW6KC+eKdmND6mJbqdDiC3BCktNnVVeoTuN9bZ56eOnmk5xMBoRERGRxi3qI6cYYx4ATgM+Au6y1n4dfOoeY8zyaJcX79SMOT6oMWp4OnrL23mghF02nTX9p3KEezPXn6pkV0RERMQpdTFM6FLgz9baA2GeG1gH5cU1n9/pCCQSSnbLMyjRDccb/EK3G3YeLZunORyNiIiISONWF82Yz62Y6Bpj5gBYqw5sFa3eus/pECQCGlE3PDVMKM/r19y6IiIiIrEiajW7xphkIBVobozJ4mBlWCbQJlrlNDRp6gsaF5TrhmdVv1tOQbEPgBSPvtciIiIiTotmM+bfAX8kkNguKrN8D/BoFMtpMFzG8OO2/U6HIRFQriuRcLsMfiAxoU7G/hMRERGRGohasmutfRh42BhzpbX2kWjttyHzW6sBquKEmjGHp8O3DGvpvvtzlrs6Oh2JiIiIiBDdZswjrbUfARuMMb+s+Ly19rVoldVQtG+Wyoy8LU6HIZFQrlue3o/KCncB4MHrcCAiIiIiAtFtxnw8gemGTgvznAWU7FbQ1fcjk81qFn/fhf69ujsdjlTBKLsrJ/R+qGa3jOCb8aJ/FDc7HIqIiIiIRLcZ87Tg7wujtc+GbnLhCxiPj88/dUGvvzkdjlRBqa5EqlWTZKdDEBERERHqYOohY8xdxpimZR5nGWPujHY5DYGxgZFb9xwocDgSqY667Ian0ZjLWPlfQMeKiIiISKyoiyFDx1lrd4UeWGt3AidXt5Ex5hljTL4xZmmZZbcZYzYYYxYHf6rdTzyyagsa85TAhKdDt4z92wBYkjrY4UBEREREBOom2XUbY5JCD4wxKUBSFeuHPAeMDbP8QWtt/+DPu1GKMabk7y1yOgSphnLd8vR+VOYLZv6JGc0djkREREREILoDVIX8G5hjjHmWwMBUFwHTq9vIWvupMaZjHcQT84q8fqdDkEMo8vpJAvYUaITdcFSxe1CJtwQ30KN1htOhiIiIiAh1ULNrrf1/wJ1AT6AXcEdwWW1dYYxZEmzmnBVuBWPMpcaYBcaYBVu3bj2Mopzj8yttiEWh2rr2zVIdjiQ2qQn+QQnzHgXAj9vhSEREREQE6qYZM8A3wCfA/4J/19ZjQBegP7AJuD/cStbaJ6y1A6y1A1q0aHEYxTnjKNcqNu8pdDoMCSN0D8LlUsPdcoJvh9enZDekwJPFTptO+5ZqxiwiIiISC+piNOazgK+BM4CzgK+MMWfUZl/W2i3WWp+11g88CQyMXqSxo49Zw7KNe5wOQ8IJ1lwq1S0v1BJhT2GJw5HEDgvM8/ekQ3aa06GIiIiICHXTZ/dPwLHW2nwAY0wL4EPg1ZruyBjT2lq7KfhwIrC0qvXjVTEe/GoOGpNCn4sqdstL8bg54HQQMSbU9z49uS5OqyIiIiJSU3VxVeYKJbpB24mgBtkYMwM4AWhujFkPTANOMMb0J1Bp8hPwu6hHGwOyzD4S96wFWjkdilQQasasqYfC0z2ag0KHSE5GJIPPi4iIiEhdq4tk931jzAfAjODjSUC1UwZZa88Js/jpaAYWi4qa9SBpxw8k714N/MLpcKQCW1qzq2y3rNDboVz3oMKSQM2ux11XQyGIiIiISE1EPdm11k41xvwKGEqgsuMJa+3r0S6nofC2PJKkHT+wcVeB06FIGLa0ZlfJbjgajfkgY3SciIiIiMSSOulcZq2dCcysi303NEkeV/C3piuJRbsLS2gGFJb4nA4lJinVDfJ5ySreTHJCS6cjEREREZGgqCW7xpi9hL/2NYC11mZGq6yGJNQ8dndBscORSFVyMtUPMxxV7AZ9cDOpFNCVn52ORERERESCopbsWmszorWvxiSU7O4vVM1hLAolc241Ty3HlA7HpGwXgOL9AMzKvoQrHA5FRERERALqZCQVY8xxxpgLg383N8Z0qotyGhK3W8lULNJozFVTzW6A9aSw06azMPU4p0MRERERkaCoJ7vGmGnADcBNwUWJwL+jXU5D4/Mpa4hFoQGYNPBQBRqNuZx9RYGWGS0zkx2ORERERERC6qJmdyIwHtgPYK3dCKiJczV+2r7f6RAkjFAyd7DZrsDBOWVVsxuQseQZEilhYKdmTociIiIiIkF1kewW20B1mAUwxqTVQRkNTnpynQyMLYfp4Dy7DgcSo6zqdgHwuTxssVl0aq7TnYiIiEisqItk92VjzONAU2PMJcCHwJN1UE6D4vMraYhFpfPsqmY3LNXsBvjwMMd/NG2apjgdioiIiIgERXPqob8D/7HW3meMOQnYA3QHbrXWzo5WOQ1OYqAmqPv+BQ4HIuEc7LPrcCAxRs2Yw2uWluh0CCIiIiISFM2a3ZXA/caYn4BRwAvW2uuU6FbjuKsByCjOdzgQCae0z66S3bDUjDnAAm6XweOukwHuRURERKQWonZlZq192Fo7GDge2AE8a4xZZoy51RhzRLTKaXBSstic2BGXSxfJsSg9fyGAGjFXFBqNWbkuAH5r1a9bREREJMZEPcOy1q611t5jrT0K+DWB0ZmXRbuchsTtMpT4/KVNZiV2JOV/C0BxZgeHI5FYZlC/exEREZFYUxfz7HqMMacZY14A3gNWAL+KdjkNSegSefOeQkfjkAr8flpu/h/5tilpzds5HU1MCQ3YpfszwObvSLYFtEjzOB2JiIiIiJQRtWTXGHOSMeYZYD1wKfAu0MVaO8laOyta5TRESQmBj2Hh2p0ORyLlFOwAYL7/CI5oqamiw1GfXWDHjwB8Q3eHAxERERGRsqJZs3szMBfoaa09zVr7grV2fxT332ClmUJGub5hzcZtTociZS1/D4BVKf0dDiT2hJrcb91b5HAksSOhpZJdERERkVgStamHrLUjorWvxiZh7wYSDPTc8hbQz+lwJGjHju00A/Z1Gu10KDEnNOqwS6MylfLovRARERGJKRoCOIbs2L7V6RCkDLPwaQCGdG/rcCSxxx1K7NSKuVRhid/pEERERESkDCW7MWRvgQaoihmFu8kq+JmdNp3BfdU8tZJgruvXCFWlTbo7Zqc6HImIiIiIlKVkNxYceQ4A2w+oZihWFHx8PwCfJo8g2eN2OJrYE2qwq1wXiryB761RM2YRERGRmKJkNxac9jAAiaaE/UVeh4MRvMWkfBX4TDLG3OxwMLFNuS7sC35nE5TsioiIiMQUJbuxwBUYJ+yPCa/x8fJ8h4ORfU+eDASmkhl5dC+Ho4ltasYMB4p9ALRqkuJwJCIiIiJSVswku8aYZ4wx+caYpWWWNTPGzDbGrAz+znIyxjrjcuNPCFwoz5k73+FgGje7cTHpW4KfwbmvOhtMDDPBhszKdcG3bbXTIYiIiIhIGDGT7ALPAWMrLLsRmGOt7QbMCT5ukFwn3wtAn3UzHI6kEfMWY544HoCZXf6Po7rlOhxQDAsNxqxsl7Tt3wHQrEVrhyMRERERkbJiJtm11n4K7Kiw+HRgevDv6cCEeg2qPh0xBoCLE95j14Fih4NpnOzK/wKw2TZj/K8vczia2FY6QJWjUcQAa8lZ9z6r/a0xGS2djkZEREREyoiZZPcQWlprNwEEf+c4HE/dSc9hQ/PjAPh6TcWcX+pD8euXA/BunwfxuGP9qxEbGn3F7oHAd7WYBBJcOmZEREREYkmDuDozxlxqjFlgjFmwdetWp8OpGXdS6Z/pLdoBUPDjl05F03iVFJJUvIsim8C40WOcjiZuaICqgBm+keRqnl0RERGRmBLrye4WY0xrgODvsEMVW2ufsNYOsNYOaNGiRb0GeFgSM6BMbZCn368A2L1DIzLXN9/aeQDM4gRaa1TdaplQn11nw4gp6UkJTocgIiIiImXEerL7JnB+8O/zgTccjKXOpWY2B+Dn7QccjqTx2fH+XQB4BpxfzZpSlgaoCvC4NceuiIiISKyJmWTXGDMDmAt0N8asN8ZcDNwNnGSMWQmcFHzc4P24bb/TITQqRe/+mRbbvgLg+ONHORxNfNm6t8jpEGJCuyw1YRYRERGJNTHT7s5ae84hnjqxXgOJAbd7nuOnnyfTbvl0tu4twJ2VS84Jvz/YdlSix1qSvn4EgA97/5VRmUpaIhEajEkVuwE+v94IERERkVgTM8muAC16ANDObINn+gEQmrnzbV8PTh01wqHAGq78t+8gB/iGnow6U9MNRcrtCtx4aez3X6z1YYCcjKRq1xURERGR+hUzzZgFSEzFjrmr3KJdqR0A2Pe/hwLTnBSrP+/hsO/dwLZ/jOPnT//N7keOJ2fh/QDkD7nF4cjiS+kAVY28QtO35FUArEv3DUVERERija7QYow59rd4f/qSfVt+hF89TdPWXeDOHM5O+B/8v04AbLn0O1q2yXU20Hi0Lx/z1T9pDvDRwemd5p34CmOGjXYsrHjW2Kce8pYUkQDs7nSK06GIiIiISAWq2Y01CUkknPMCTf84l6bte0FCEvtaDym3ysanz6XY63cowPhVMu9JAIqtG4B9CU3Z+NvFDFKiW2Oh1suOd1W1Fnxex4rfdaAEgBI8jsUgIiIiIuGpZjcOpJ/9JFsXvM4OTyu6f/RbjvItgTuzyDvmDnqfdpXT4cGKD/CunMOGAg8tvRtJ/uE1ANZctIROuR0cDi7AOrtcVQAAIABJREFU9/3beD7/fwC8N+5LTh94BOkuF+kOxxXvrJMz7e7fxr7/nE/6hs8ByDtvCb271O/xtmPnDloBXVqk1Wu5IiIiIlI9JbvxoEk7Wpx4JS0AevRl/3O/Iu3AenovvIWtSx5j98i7adt7GCmZzSpv6/Oy551b2OVPYV/uCDr3HUKyx33osjYvpcR48LTsHlFovs8ewj1nGglAxTSj0zP9+HDk24waPiywwO/H7t3Ivpf/wJ7dO2i7bylLBz9AnzEXR1RWjRTupuTbV/G8dw0AoVf8vut4xv+iu0ZWOkwm+P451op5xX/hP2eWu1nx9bNT6X3ny/UXw9bl9FrxGAC92of57omIiIiIo5TsxpucHqRdn0fhd2+QPHMyLUo20uKDyfABLOlzI/3OuOngutZS/FB/MveuIxNg8f3wJnzY7ByMcdHm5Km0STf89PkrlGxdid2/nWP3flipQebC3Is4ZvI9FD4xCrt7A3vPm01Ou86w8yfcc6ZVCnHhqf+lZ959pK75L6M+OpWlXw3miAOLSLRFGCAj+APQZ+415C96mJ2nPk33PgMCYX//BsWvX8HmnOPocMkMKCmAPRvAWvwLnsX0PxvT+shDv0c/vAsvnlPpddzQ7GHuuuL80kRNDp/foXbM/v9MwgV8knQC7cddTedZp3Nhwgc8NONN/njO+PoJYv5TAMy2Azkpq2n9lCkiIiIiETO2gQ0wM2DAALtgwQKnw6ja+gXw1ImQmAE3r6/9frzF/Pzi1TTd+CmZB34GYF1KT7L/+Bmp+36GR44uXXVzSjdaFP6E25YcbvQA7LxqNVl/6wJACQn4L59P4uLp2A7H4TriJPD78f1fG9y+AnaThgHSOcDXqcezvtkghv7qCpqs+4jU1ybXqvxl49+i59HDKz9RvB/uagPA895RJA08n9yk/bTv0pu2XfvV9uVKBfalyazMW8B7w99gyqhu9VZuSd6beF45r/Txnhu3kZnswTfjXNzL3wZg59StZKUl1nks2579NZ6fPuaK3Fk8f/Ev6rw8EREREQkwxiy01g6obj3V7MazhERyf/MoAAUfP0DKJ3+hfcEy+Gvz0lUKSWLjRQvonBscvXnvZti3BR4/mCiu6n8DzXJ740nJJK3zQFxFuylZ8zkJPU7G+/XTeObcWqnoUKK7hzR8160hKz0JTvpL6cBFuFy4b9kMQJMy2w0qt5PToc9Oflz0IZlzppJRsIEEfLyRMpFfFsys8qX3fPM0ls47lU7DzyO19RGY9ByKnh1P0uaFACz2d2HCn18kI1kDB9WJ4Ae9ryg6N0+q5S2i4NnTSdkwt3TR8snf0j34+brPeYHi/2tPYskenp/9FVdNGHb4Zfp9gZ+EMInzkpdpvvYdivBw88k9D78sEREREYk6JbsNRMqIa2Do70prNQF+SutHm6s/oXNCmUG3M1oFfm7aAPnfQ9tj6Oqq0Ic3KQ3PkWcB4Bk2heLsIzhQXEJmn5NxFe6A+w7W5JVcu5rs9KTaB+5y0XnAaBhwcETkXwIU3I/95gXMf//EYldvEgdeyPKVKzj+ov+j2b0tAOiT/za8+vbBsIO/t9imbDvrTfor0a0zoZsam/cU1X1huzfAg71ICT6cM+R5Rp50Gt0rNEd3j7kT3r6KvUvegmgku8+Mha0/wA0/QdnvyIEd8NolACzwH8GQVhnhtxcRERERRynZbUgS0+C23YFRg4yhY1XrJqVD+4GR7bbXOErrttJzYMq3FG76gaRux5PtSalq09pLycIMuQKGXEH/4KJeYwO//ZNeYF/+zyzcavF//zY9fT+Q5+/ETptOxwufone7ZoxK0qFd14wxZCbX/fvsf6hf6RxpP1y6hhPbhB8Myt19DLwNZ3vfxNp7Dq9vtrcY1n8NQGFREckpqQefswen/VrrasdQ9QEXERERiUnKCBqiur74zupIclbHui2jCq6ep5LZE0YAcDkAzb1+EhM0bXR9chvw11Wf/4Jd4PZQ/M4NJNrAPLo/X7mJHtmph94mvSUAiXhZvG4XR+Vm1b78zUsOhvLAUViPm8Srv8HtSYJ1X5c+18WdX/syRERERKROKTuQBkGJrgOMwe+vfrWIbcmDHT/i/+pJuKcD3NWGxG+fB+D1Ye+QW1WiG4xne5cJtHdt5eXPvz+sUEqWzir9O6tkMykHNrDroaGw4Bn2znu29LkOx//msMoRERERkbqjml0RqTVftGp2SwrhsSHAwTtwxdbN1pTO/DzkLiYOPy6i3TTLzoHVkJj3EjA04uK9X/wd30f/x5bmg0kcdyeeZR+TDcxvcx7Hbgwk3Nn7V8LbV5dOm/XFpO8Y2jM34jJEREREpH6pOkxEasUQvWbM3mdPLvf4ae843NO20/bGBQwePvoQW4WJadBlAPzFM53PV26LaBv7znUkzP4TSb4D5G6ZQ6vnBpO9eykAHc65D66oPJXZYn8XBnVrU2m5iIiIiMQOJbsiUmt+fy2SXb8f5v0TZv4WNiyC3Rsoyv+x3CpDLvsnblct+p4364S3aUcAPn//xYg28S36NwD/an87a4fchY/AyMtf+XuQk5EMzSvPI9z/9kX8f/buPD6q6v7/+Oszkz2EEAIBTFgFQRRFiSiIK6IoCqLi1rrUqnWp1db6rV0Uqm21X/2prbV8xQVBrVbFBRVFtG5VETCACMouEvZ9SUKWmfP7Y4aQnWwzN8v7+XjkkbnnnnvPJ5PLJZ85557jj9HAGBEREZGmTH+tiUi9bdy9r24HfPr/4IN7DmwvehmAZKDY+Ym5fTEWKOTwtHb1jinmkmfh8ZM4b8skSgI3EeOv4TO9LUuJCRTwUeBoxlx2E6lJsdD7CJg6hr4njj1Q79J/waYl0PU46HxUvWMTERERkehRsisi9RJwjn3FdZyhqmyiG7aVVDqwi6+P+h2D2nZpeGBdQsnoEb41PPnZKq49uXf1dfNCQ53fdkM4NSm8LnOvU+F/VtMuscxszv1Ghb5EREREpNlQsisi9RLn9xFfx1mw16cOJLBjHR9k3sAlXTYRN/RGOnToBcCgRoytpOsQYtZ+Qd5nj8PJD1RbL5jzLD4gudOh5XckVb2Wr4iIiIg0H0p2RaRefPVYZzevsITNLoNLr/klCbH+CEUGMZc+Bw8cSufidTXW210UpB3Qrs/QiMUiIiIiIt7QBFUiUk9GoC4TVBXupc++b/CZi2iiC0BiqGf2vMD7lASqH2qdsuw1Nrl2DOyZEdl4RERERCTqlOyKSL2YUadk1338VwCK22RGKqQDfD7WJx3OdlL4flt+tdVKiKGYGI7Kqv+EWCIiIiLSNCnZFZF6W7Jhd+0qOof7YiIA83vdGMGIDrCOhwHw3cbqY3QuyDuBwbRPjotKTCIiIiISPc0i2TWz781skZktMLN5XscjIqE1dtsl1TJJnPk7fK6YZcFMzj/thMgGFtYm1pFlW9m65ruqK+zbTYLbR0xsfFTiEREREZHoahbJbthpzrmBzrlsrwMREYiL8eFqO0HVkjcA2ODS6Z6eHMGoDkjqcxIAOzZ+X3WF5e8B4E9sG5V4RERERCS6mlOyKyJNiBmU1PKZ3U2pobVv/9Pnd5EMqRx/Rj8A9hSWVF0hGCr/tv0Z0QpJRERERKKouSS7DnjPzL4ys+sr7jSz681snpnN27JliwfhibRGxs784lrV3JIf5PtgJ646+6QIx1RZ5+1zqywvKA4A0KmtntcVERERaYmaS7J7onPuWOBs4GYzO7nsTufcJOdctnMuu2PHjt5EKNLKxJfsAaCopPqlfdi7Bb58nCO3zcRHkB5RGsIMQIe+ALQv3ljl7sCXkwDIbN8maiGJiIiISPQ0i2TXObc+/H0z8Bow2NuIRFq5onxSS7bS1TZRWBKous7mbwm+cCm88z8AJPgC+HwWvRhTOrEzthPOQV4VQ5nzgqGJqbr3Ojx6MYmIiIhI1DT5ZNfMks0sZf9r4EzgG2+jEmnl+p4NQBp7KQ5U8dzu5u/gnyfgW3dg8vS3if4Q5lif4zT/Ar7O3VVpX0FxgDnBvnRNT4p6XCIiIiISeU0+2QU6Af81s4XAHOBt59y7Hsck0rqlZpW+rKrX1L1a/tH69YN/z6W3/CXiYVWUENhDB9vNqq17y+9wjh6752E4OqUkRD0uEREREYm8Jp/sOudWOeeODn8d4Zz7s9cxicgBVU1StbMo9P2VTrexa+jvOOSc/yExPatSvUgrOf5mABYsXVl+x+71ACRQHN2h1SIiIiISNTFeByAizZcPR1GgwjO7gRLSti/kk8AAhlx2J6ntEr0JDohP6QBAl83/BU4/sCMYStBnJZ/HAA/iEhEREZHIa/I9uyLSBMWEJne6xP8hq7bkld+XvxWAvSSS6WGiC5Q+W7x1d3654r1z/wVAWmrbqIckIiIiItGhZFdE6q77MACKicFfcRhwYWhJooWJx0c7qsp8ocErl/MOJYHwEkklhbT5/K8ApA260KvIRERERCTClOyKSN35YwjGp3Kmfx7LNpWf/Cnw1VQA2rbP8CKy8tpmArDHJfHthlAS7lb+B4CdLpnzBvX0LDQRERERiSwluyJSL1acT2fbQVFJsFz5rn2hZ3hLDh3hRVjlmbGz43EM8S8hZ+U6AIpfvg6Avx/yQOVeaRERERFpMZTsiki92LDbCDhjdYVlfVIXPA5A3y5pXoRVSZsuvQFYv2YlwS+fIK4k1MN7+9WXeBmWiIiIiESYkl0RqR9fLH5zrNy4q1xxnrVhs2vHSX06eBRYeTFHjgUgc81r+N75NQBPH/U8yfGajF5ERESkJVOyKyL1Y6EhwMfvfY/gH9NhQiq7lnxA2+BO3g8c23SSyV6nApBYuAWAdwPHceWYc7yLR0RERESiQsmuiNTPYWcB8EDsJHyuBIDUly4AoHNyE3oWNrxM0riYTwBY1u5EYvy69YmIiIi0dPqLzwsxCaHvhwz0Ng6Rhkg5pNpdCSMnRC+OOjpzlJ7VFREREWkNmsg4w1am0xFw5RuQ0d/rSETqr01HOPoy3Nf/ZsfpD9C+7zBKdvxAYXImQ7OO8Dq68u78geAnDxHIGky/fvp3JyIiItIamHPO6xgaVXZ2tps3b57XYYiIiIiIiEgEmNlXzrnsg9XTMGYRERERERFpcZTsioiIiIiISIujZFdERERERERaHCW7IiIiIiIi0uIo2RUREREREZEWR8muiIiIiIiItDhKdkVERERERKTFUbIrIiIiIiIiLY6SXREREREREWlxlOyKiIiIiIhIi9Mskl0zG2lmS81shZnd6XU8IiIiIiIi0rQ1+WTXzPzAY8DZQH/gMjPr721UIiIiIiIi0pQ1+WQXGAyscM6tcs4VAS8CYzyOSURERERERJqw5pDsZgJry2znhstEREREREREqhTjdQC1YFWUuXIVzK4Hrg9v7jWzpRGPqmE6AFu9DkJaHF1XEim6tiQSdF1JJOi6kkjRtdW0dK9NpeaQ7OYCXctsZwHry1Zwzk0CJkUzqIYws3nOuWyv45CWRdeVRIquLYkEXVcSCbquJFJ0bTVPzWEY81ygj5n1NLM44FJguscxiYiIiIiISBPW5Ht2nXMlZvZzYCbgB552zi32OCwRERERERFpwpp8sgvgnJsBzPA6jkbUbIZcS7Oi60oiRdeWRIKuK4kEXVcSKbq2miFzzh28loiIiIiIiEgz0hye2RURERERERGpEyW7UWZmI81sqZmtMLM7vY5HmjYz62pmH5rZt2a22MxuDZe3N7NZZrY8/D0tXG5m9vfw9fW1mR1b5lxXhesvN7OrvPqZpOkwM7+ZzTezt8LbPc3sy/A18u/wpICYWXx4e0V4f48y5/htuHypmZ3lzU8iTYWZtTOzV8zsu/B9a4juV9JQZvbL8P+B35jZC2aWoPuV1IeZPW1mm83smzJljXaPMrNBZrYofMzfzayqJVQlipTsRpGZ+YHHgLOB/sBlZtbf26ikiSsBbnfOHQ6cANwcvmbuBD5wzvUBPghvQ+ja6hP+uh6YCKEbOTAeOB4YDIzffzOXVu1W4Nsy238FHg5fVzuAn4bLfwrscM71Bh4O1yN8LV4KHAGMBP4Zvs9J6/U34F3nXD/gaELXl+5XUm9mlgn8Ash2zh1JaLLSS9H9SurnGUK//7Ia8x41MVx3/3EV25IoU7IbXYOBFc65Vc65IuBFYIzHMUkT5pzb4JzLCb/eQ+gPx0xC182UcLUpwPnh12OAqS5kNtDOzLoAZwGznHPbnXM7gFnoBtyqmVkWMAp4MrxtwOnAK+EqFa+r/dfbK8DwcP0xwIvOuULn3GpgBaH7nLRCZtYWOBl4CsA5V+Sc24nuV9JwMUCimcUAScAGdL+SenDOfQJsr1DcKPeo8L62zrkvXGhSpKllziUeUbIbXZnA2jLbueEykYMKD8U6BvgS6OSc2wChhBjICFer7hrTtScVPQL8DxAMb6cDO51zJeHtstdI6fUT3r8rXF/XlZTVC9gCTA4Pj3/SzJLR/UoawDm3DngQ+IFQkrsL+Ardr6TxNNY9KjP8umK5eEjJbnRVNW5f02HLQZlZG2AacJtzbndNVasoczWUSytkZucCm51zX5UtrqKqO8g+XVdSVgxwLDDROXcMkMeB4YBV0XUlBxUeHjoG6AkcAiQTGl5ake5X0tjqei3pGmuClOxGVy7Qtcx2FrDeo1ikmTCzWEKJ7vPOuVfDxZvCw2UIf98cLq/uGtO1J2WdCIw2s+8JPU5xOqGe3nbhYYJQ/hopvX7C+1MJDQPTdSVl5QK5zrkvw9uvEEp+db+ShjgDWO2c2+KcKwZeBYai+5U0nsa6R+WGX1csFw8p2Y2uuUCf8AyCcYQmSpjucUzShIWfM3oK+NY591CZXdOB/bP/XQW8Uab8yvAMgicAu8JDcmYCZ5pZWvhT8jPDZdIKOed+65zLcs71IHQf+o9z7kfAh8BF4WoVr6v919tF4fouXH5pePbTnoQm45gTpR9Dmhjn3EZgrZn1DRcNB5ag+5U0zA/ACWaWFP4/cf91pfuVNJZGuUeF9+0xsxPC1+qVZc4lHok5eBVpLM65EjP7OaF/JH7gaefcYo/DkqbtROAKYJGZLQiX/Q64H3jJzH5K6A+BceF9M4BzCE28kQ/8BMA5t93M7iX0gQvAPc65ihM0iPwGeNHM/gTMJzzRUPj7s2a2glAPyaUAzrnFZvYSoT88S4CbnXOB6IctTcgtwPPhD3RXEboH+dD9SurJOfelmb0C5BC6z8wHJgFvo/uV1JGZvQCcCnQws1xCsyo35t9UNxKa8TkReCf8JR6y0IddIiIiIiIiIi2HhjGLiIiIiIhIi6NkV0RERERERFocJbsiIiIiIiLS4ijZFRERERERkRZHya6IiIiIiIi0OEp2RUREREREpMVRsisiIiIiIiItjpJdERERERERaXGU7IqIiIiIiEiLo2RXREREREREWhwluyIiIiIiItLiKNkVERERERGRFkfJroiIiIiIiLQ4SnZFRERERESkxVGyKyIiIiIiIi2Okl0RERERERFpcZTsioiIiIiISIujZFdERERERERaHE+TXTMbaWZLzWyFmd1ZTZ2LzWyJmS02s39FO0YRERERERFpfsw5503DZn5gGTACyAXmApc555aUqdMHeAk43Tm3w8wynHObPQlYREREREREmg0ve3YHAyucc6ucc0XAi8CYCnWuAx5zzu0AUKIrIiIiIiIiteFlspsJrC2znRsuK+sw4DAz+8zMZpvZyKhFJyIiIiIiIs1WjIdtWxVlFcdUxwB9gFOBLOBTMzvSObez3InMrgeuB0hOTh7Ur1+/xo+2MRXlw9alYH7octSB8uJ82LKUdb4uZHbu7F18rU0wABu/BmBfx6NIiPV7HFDTF9i2muLCfIrSDqNtYqzX4XgnWAIbF7HF15GOnbO8jkZERESkVfjqq6+2Ouc6Hqyel8luLtC1zHYWsL6KOrOdc8XAajNbSij5nVu2knNuEjAJIDs7282bNy9iQTeK3Hnw5HCIS4HflYl1XQ48cRrXFP2Mp/9yl3fxtTKFT40ifu0qVga7cOg9870Op1nYPfVyNq5YwMpx73L2gC5eh+OdvG3wQC9e6HAtl/38T15HIyIiItIqmNma2tTzchjzXKCPmfU0szjgUmB6hTqvA6cBmFkHQsOaV0U1Sg8c7VtFXmGJ12G0Gpu2bAFg7UVvexxJ8+PN9HZNT3Kcl58bioiIiEhVPPsLzTlXYmY/B2YCfuBp59xiM7sHmOecmx7ed6aZLQECwB3OuW1exRxxbUOPLN8a8yqr3u5Br68fKt2Vd+syktM6eRVZy5W/nW77lvJ5oD+nHnWo19E0G/ufQfBoMvcmw+EwIBAMeh2KiIiIiFTg6Tq7zrkZzrnDnHOHOuf+HC67O5zo4kJ+5Zzr75wb4Jx70ct4Iy6lE7u7DQcol+gCvP5xEx+a3UwVLn0PgNzEJv6cdxPlWnnfbkkw9PPH6zlvERERkSbH02RXKkvpe2rp648CR1N81v0A5C3WENtG4RyBzcsgUExg/r+If+NnACQOHOtxYM1Tq+/ZXTYTgJR4DWMWERERaWr0F1oTYwPGwazQ5FSpJ/6E2KNGw8w76b9vAaz+BJIzIEO9kPUSDMA97fEDu3xppAZ3APBxj1s575zR3sbWzFh4HHMrz3VxeVsByO14iseRiIiIiEhFSnabmrZdYMIuAI4pUzzMvximnAfAimuX0jtLSxPV2e4Dk33vT3S/9ffllKvv8SqiZs+18q7dYHgYc3F8mseRiIiIiEhFSnabgV3dzyR1zXul24teuIvedzzhYUTNU2D9QvzAbpdEW8tnW5vD6HPb516HJc3Ynn0lJAKFJZqgSkSkPnbv3s3mzZspLi72OhQRaQJiY2PJyMigbdu2jXI+JbvNQOrxP6Zo4zy2+TvSJX8ph+6Zy+Zd+WSkJnkdWrOyMedtMoE3jv4/rjjlSNLb9zowHlfqxfOOXedCw9P93tzKtuUVkgEc2jHZk/ZFRJqz3bt3s2nTJjIzM0lMTMT0f7JIq+aco6CggHXr1gE0SsKrCaqag/5jiPvtarr8zxyKYlI4yreajIe7MH9+E5mhuXhf6Gvzt7jVn8CEVNY9di7BQBPq7crfTuaKfwEweMipkH6oEt1GsHZ7vqft5z17OdybDhNS2bpjZ9TbLwr36HZN1wdPIiJ1tXnzZjIzM0lKSlKiKyKYGUlJSWRmZrJ58+ZGOaeS3WYm7tavcBb6tR3zxnDWbt3tbUCBEtz93eDPneCfJ2Dh54ozt3zKur9mlz7TWElRHuzbFZ0Yf5gN/9sTgIWxR9O3S2p02m3B4vyhazDJq1mI9+0m/43bSV41o7TopZefj3oYGWvfBaBdQmzU2xYRae6Ki4tJTEz0OgwRaWISExMb7dEGJbvNTUonbPwOAv4EALr+oysLnv89Ozetrbp+UT57Jp1DwYvXULxxycHPv/lb3LaVtQ6n8NmLsUBhubLZ/kGh2IpWMm1a+QTEFe6l+M9Z8JdD4P5ufPLEHbVuq0727WLjfybChFR4+iwAlvl70+fXH0SmvVZm/yfwXk1QFfjmVZLmPwnAxphMAA5Z+1Z0g1jzBV32LgagQ/t20W1bRKSFUI+uiFTUmPcFJbvNlP/O70tfD1z+D9pNPJI5X1Ue1rzvy6dIWf8Zid9NI/b/hrD2zwPZuGVr+UrOQUkRbF8d6p199Fi+ff43BP/YPjQkeWE4QcyZCt+VWe+3cA/x31dOHg/7xRsEr3kfgHGLb2bBZzPZ9ModMCEVuy+T2OI9pXVPXjeJme9VWEN451q2Tr2KFTMn1uk9KXs893ej8yd3lis+9PfzSIpXD1xLsG7O6wA81PMJOv8h9CHO+f7PmbN6e/SC+OYVAO4svhbz+aPXroiIiIjUiiaoaq5iE+E3a8ifdBZJO74DoN/08ygeuI7Y8BBT9u0i4YM/lDusa/FqeOxQAOYccRdH7f6YhLWfVDr94cv/r/R15msXUNT2HeKm3wLAW5m3cu5191A8bwr7U8dt494gfe6D0Pcc2qemQJsDCycNnHVxuXMvybqYHj9+FN8X/yDh43s56/PLmbvoDDLOvYs2278hfebNdAA6rHqdvIFnkvDDJ2z6fgmpqakkf/4AP2SdR7drn6v2rcmfMo4kINc6wyXPkbVvOWQcjt+nT48bS+k6u1507OZvp9vmD9nu2vCTi84HoCi5C3F5G1j7/M0M/sMLUQljV0ExJS6FHf0ui0p7IiLS/AQCAQD8fn0oKuIFJbvNWWI7km79MvR6QiptLZ+1f+pP+98sInnXcpg4BIC5SSdx3P+8BSVFbPvXdaSvCvWKDV58b6VTftzxRwyI30z73Fl8dshPOHH9ZADippxdWmdE7j/Zsf1W0mb9HoA3T3+P8444Ho449cCJ/DFw1Vvkfz+P3I0bKbQEOo24lYz09vTfX+e0X1NQuIe42Y9y3J734YX3K8WTPPFYAA4pU9Yt900+/HAWp502ovJ74hxJO74FYOe1czgyMxU4rub3UeotGOVst+TracS8eg0AS5MGMSQ5DoC469+Hh4/gwpIZ7CooJjUx8j34m3fvIx3HBcdmRbwtERFpnoYPHw7ARx995G0gIq2Ukt0Wwl3+MvavcXR1G5j1j2sYnvc2PuDL+KEM/GUouSUmjvQrp4ReTzgwSdPSc6fRd9BwMOOUMuc8EQh8OwL/vy8HYDupJMUESSjZQ/zfewHwZbA/5540uOqgep5EUs+TOKyGuBNH/hFG/pFdM/6IP2cyu9v0ouToH9Nlwd+J3bW62uO6fvwrOOkriIkrLStZ9SkxU88FYHrwREZnaiKqSItmqus2f1ua6C5OPI4Bt716YGdqFjuTe9EubxVfLf6W07OPing8fX74N3nEc2y3tIi3JSIizdPjjz/udQgirZqS3RbCDjsTbv0a/nYUI/ZOLy3vc8u5t4KBAAAgAElEQVRrxMdW8Wu+bRFFyz4g5uiL6Rtf/Rqh/n7nkJ99E9v3FJA++k8kbP0aJh/o5Y2/+tVGeYg89ZzxcM542uwvGHAS7t9XYFtCvbR7up6Gb9cPJN02F7unPb35Af7Ukc3x3dl+zM85rN+RpYkuwFHX/l/lRqTR7P+NR61jd8ca7J8nAPBup+sYeeODlaoUZv8MPv4NC77fxunZjdDm3i1QnA9p3cuXB4ph6hgAVrsuHJkS3wiNiYhIS9S3b1+vQxBp1ZTstiRp3XHXvMeGWX9nh2tD59HjSW+TUHXddt2IG/yTg5/TjKRz76N0FdHkoezpcAwpW+cz59BfMLhXl8aKvrwOfbCbvsDNfYpgRn9SegytslpG4RoyZt8Bs0Pbq2N7k3reX+jRrVtk4pJyojKMOVACfwv11H7cdjRn/ex/q6zWMTX0UUnSireA0xre7sShkLcZ7toWGpa/375dsOYzAObYAI5seEsiIiIiEgFKdlsY63Y8h/z0+XLPuDa2lJs+gOICBse3OXjlhjDDBl9LpSkdrpkJ+dtwWYPZ+fE/Scx5goTAHla3HUy3297TRFTREMW3eO9rt5b2+B/9sycxX9WTyPsOHwXTbyahaFvjNJwXXszcBajuVqlLTURERKTp0tJDUnc+P0Q60a1JtxOg3yisTUfSRo0n4a5cmLCLnr+apUQ3SozorbPb5pvQzNtvjllIu+QahgzHh57RvjTwVsPjyj+whNGKzXuqrZaslaxERKSM3bt34/P5mDRpEgCTJ0/G5/Oxd+9eIDRh1TnnnONliCKtipJdEam3SOe6hXNDE6qtdIdw3jE9aq7s87Ep4VB2kMLmPYUNa3jbitKXec9fReG0mw7s2/RN6cv0fWsb1o6IiLQo8+fPxznHMceElmDMycmhT58+tGnTpnT/scce62WIIq2Kkl0RqbP9/efBSCS7hXvYO/UyNk08j/i3fwHAwhMn1urQvRnHEEOARbm7GhRCsKig9PXRef8lftHzLF/4RaiN1XNL9w3OrmYmchERaZVycnKIiYlhwIABpdv7k9vVq1ezY8cOJbsiUaRndkWk3lxjLj60YSHEJpE39znarJpR+pzu9/H9GDvilBoP3a99SjJptou5X3/DGf071TuUvV8+Q9sKZX1eGwlrr2HvhlzaAB+e+CynnXZWvdsQEZGWJycnh/79+5OQkEAwGGThwoWcf/75pfsAJbsiUaRkV0TqLty126jDmB8/GYD9C2F9mHEFvQYMo/uJ42q9vFW7rL6wGHbmfgcMr33bu9eTP+MPBAZdQ0qfYWwr9NMW+OT0aZz8nwsP1Jv3NJ3DLzP7nQAxWnZIRKQx/fHNxSxZv9vTGPof0pbx5x1Rr2NzcnIYPDg06mfp0qXk5eWVJrfz58+nffv29OjRo7FCFZGDULIrInW2P/VcurH6yZvqZMkb5TbfCA5j9I2P1nkNZ8scBMBJu6YDN9fuoLxt8NDhoeW1vpvGljb96Ln3O7a6tvQ+6kTo+T48dUalw3pkVOz7FRGR1iwYDLJs2TIuvvhioHJP7qxZsxg6tOqlFEUkMpTsiki9dUiJa5TzFHz0EIlltncMuqXOiS4AWccBcK5/Nl+u2sbxvdIPekjwvd+Xm7yg497vAEikkA7tEiG58kq6e37yMSnx1axhLSIi9VbfHtWmYP9KAOvXrwdCyW6PHj1IS0tjxowZzJkzh2nTpnkZokir4+kEVWY20syWmtkKM7uzhnoXmZkzs+xoxici1fP7rP4TVDlXbnmfzQXGwmCv0u0fjxpRv/P6/OztFBo+9vl//1OrQ4q+ew+Auwd8AHesZHtKXwA+Dh4dqhCbWOmYlO4D6xefiIi0WH6/nwsvvJAnn3ySa6+9lpkzZ5KYmMh1113H2LFjueGGG7jgggu8DlOkVfGsZ9fM/MBjwAggF5hrZtOdc0sq1EsBfgF8Gf0oRaQ6Rj2f2d23G+7vCkDw2KtwWcfTfc98dnAo7vqPsKI8YmL89Y4r+YzfwPMX0ib3Y2BczZWDQRIKt/Fh4Gh+fuaRkJxAm9Nvhzeup39muwP1jrsOvnsbjr0SBlxU79hERKRlmzJlCoMGDeKVV15h8eLFdOvWDYCPPvqIIUOGeBydSOvj5TDmwcAK59wqADN7ERgDLKlQ717gf4FfRzc8EamRHRiyVWsf/gU+/mvppi9nCuSE1tId6FsJhxzT8LC6h/6YOGHfpwevvGEBAMXEkJESGpYc130w9B5Bj2G3Hag36sHQl4iISA3i4+O54447uOCCC+jduzePP/44I0eO9DoskVbLy2HMmcDaMtu54bJSZnYM0NU591Y0AxOR2jCCdU12yyS6+xWHP3Nbc8I9jREUxCWzPaYTe1wSm3bvq7luSWj/uwnnHChr3xN+/Ar0OLFx4hERkVZHywyJNA1eJrtVzT5T+pezmfmAh4HbD3ois+vNbJ6ZzduyZUsjhigi1TGo8zO7e9oPAGCXhWYyzj/9T8RO2AYTdtF95K2NFptL6cJQ/xLeW7DqIBVDP8Ah6SmN1raIiEhOTg6ZmZlkZGR4HYpIq+ZlspsLdC2znQWsL7OdAhwJfGRm3wMnANOrmqTKOTfJOZftnMvu2LFjBEMWkbLq2rO7q8TPZ4Ej2HLjEpiwi6STb4lIXKndQjMoxy16ocZ6RXMnA9AmKSkicYiISOt03333kZub63UYIq2el8nuXKCPmfU0szjgUmD6/p3OuV3OuQ7OuR7OuR7AbGC0c26eN+GKSCV17NktKCoB4NCObSIQzAExZ/4RgG1bNtZYL68kdAts20eThoiIiIi0NJ4lu865EuDnwEzgW+Al59xiM7vHzEZ7FZeI1F5de3Z94bVz67WGbl0ktgegna+gxmpFJUHWu/akJidHNh4RERERiTovZ2PGOTcDmFGh7O5q6p4ajZhEpHbM6vjMbqCEQwsWsSvuqIjFVNHYwMwa97dbM5Od+OjavvJauiIiIiLSvHk5jFlEmrllm/bUvvLONQCUWGyEoinD52NdfG+20ZbCkkC11eJLdpPMPrq11zO7IiIiIi2Nkl0RqZeSgCMtKa729b8Krac7v92ZkQqpnPy0vgSdsWlXYbV1gvh4OjCStglRSMBFREREJKqU7IpIvcTH+nB1mKHK98VjAFjvEZEKqZy4GD8AuwqKa6yXGOPH54vwM8QiIiIiEnVKdkWk3oLBWlYsysPnSpgXPIwzs/tFNKb94mN8pNtuVm3dW2O9wkBtfwgRERERaU6U7IpIvRh1mI35tZ8BEEMJPTpEZ+bjNr4ikq2QlF3Lqq4QKMZHkE4p8VGJR0RERESiS8muiNSTEahtsvvDlwCk+mseUtyYSg4fA8D6DeurrrD0HQD8GsEsIiIi0iIp2RWReqnL0kOF7fsAMLf7tRGMqLzU9C4AbNlbzQRVxfkArM48L1ohiYiIiEgUebrOrog0b8UltXvedZNLIxjsRPzAcRGO6ACzUJdtrK/qz/SCzuED9gXUtSsiIpERCISWv/P7/R5HItI6qWdXROrFOfh24+5a1d0dnhF5yKHpkQypSnlFJVWWF4YT9V4do/MMsYiItD7Dhw9n+PDhXoch0mqpZ1dE6sUMOrSp3eRO+3tZM1ISIhlSxVYBOHzrTKDy8OndBUUkUvuh2CIiInX1+OOPex2CSKumnl0RqZcYn1Hb+akSdq/Gb1HOKjMHARAsKapyd+ziVwDonq6eXRERiYy+ffvSt2/fOh1zyy23cN555eeTePrpp+nTpw9xcXG0a9euMUNsVM888wxmxooVKyqVmRnLllVeIeGjjz4q3f/++++Xll999dVkZWVV2c7+Y8rWb46ay++1sTz88MMcddRRBGu9dmXDKdkVkXrpVPg9Fqx6iHCpr1+GCakcWrSUVGpe77bRxSWxxZ9BsJquWxeO3ZfWLZpRiYiIVGvlypU8/vjjjB8/vrRs/fr1XH/99QwdOpT//Oc/zTbBS0lJ4dlnn61UPnXqVFJSUjyIyFst5fdaFzfccAObN29mypQpUWtTya6I1F0wNOFGr+Dq6uvkb6fo61dKN2f6T4l0VJXE+X0Egq7KhDcYdMwJ9qVj26SoxyUiIlKVRx55hKOPPprs7OzSsuXLlxMIBLjqqqsYNmxYuX0VFRZWswJBE3DBBRfw3HPP4coMCysoKGDatGlceOGFHkbWeOry/tfl9xqJ9r2QmJjIlVdeyYMPPhi1NpXsikjdDboaAF+wmnVzSwrhf3sSt+Ld0qKOKXFRCKxqW/Mq3/zzi0MJe2KcZsgUEZHGsXv3bnw+H5MmTQJg8uTJ+Hw+9u4NjW4aPnw455xzTpXHFhYW8txzz3H55ZeXll199dWceuqppceaGVdffTUAEyZMwMz45ptvOOuss2jTpg0XX3xx6bHvvvsuQ4YMITExkdTUVM4//3yWLl1aun//8d999x1nnXUWycnJdOvWjcmTJwPw7LPP0q9fP9q0acNpp53GypUrG/TeXHHFFaxZs4b//ve/pWWvvfYagUAgIsnuihUruOKKK+jZsyeJiYn06tWLG2+8kR07dpSrt2zZMsaOHUtGRgYJCQl069aNcePGUVJS8+i1mt7/hQsXMnr0aNLS0khMTOTEE0/k008/LT22pt9rbY5vaPtlj1++fDmjRo2iTZs2dO/enXvuuafSMOOFCxcyduxY0tPTSUxMpG/fvtx3332V6hysTYBLL72UJUuW8Pnnn9f4/jYWJbsiUncWunUUFgcoCVTx3MXi18pt7ugyjOPH3BiNyMqJsxLGxXxC7o6CSvv2FYfi7pKaGO2wRESkhZo/fz7OOY455hgAcnJy6NOnD23atCndf+yxx1Z57OzZs9m5cycnnXRSadldd93F3//+dwAee+wxvvjiC+66665yx40ZM4ZTTjmF6dOn88tf/hIIJbr7E5h///vfTJw4kW+++YZhw4axbt26csePGzeOUaNG8frrrzNo0CCuueYafve73zFx4kTuv/9+Jk+ezNKlS8sl4fXRvXt3Tj755HJDmadOncrYsWNL35/GtH79erKysnjkkUeYOXMmd999Nx988EGlDxvOPfdc1q1bx8SJE5k5cyb3338/8fHxtX6utOL7n5OTw9ChQ9m+fTtPPPEE06ZNIz09nTPOOIOvvvoKqPn3WpvjG9p+WWPHjuX000/n9ddf5/zzz2f8+PHlhhnPmTOHIUOGsHLlSh5++GHefvttfvWrX5Gbm1tapy5tDhw4kLZt2/Luu+8SFc65FvU1aNAg1+Stnevc+LbO/TnT60hE6mfFf5wb39ZddOeDbu++4kq7i/+WHbrG9395ZNejpzg3vq3715drKu0LjG/n5tyVHf2gRETEOefckiVLvA6h0T300EMuJibGFRQUOOecGzp0qLv00kudc86tWrXKAW7atGlVHnv//fc7M3OFhYXlymfNmuUA9+GHH5YrHz9+vAPcI488UulcgwYNcr1793bFxQf+j161apWLiYlxv/zlL8sdP2XKlNI627dvd36/37Vv397t2rWrtPxvf/ubA9z3339f488/efJkB7jly5dXWfbUU0+5du3auYKCArd+/Xrn9/vde++95z788EMHuFmzZpUed9VVV7nMzKr/Vq6q/sEUFxe7Tz/91AEuJyfHOefcli1bHODeeOONWp9nv+re/9NPP93169ev3O+xpKTE9evXz40ZM6a0rLrfa22Pb2j7+49/+umnyx1/5JFHuhEjRpRun3TSSS4rK8vl5eVV+17Uts39hg0bVq6Nqhzs/gDMc7XIDdWzKyJ15wsN/fXhKKniedg9LtRbOvvov8Co/xfV0MqyQ08DIFhx2ug9G/ERpIO/co+viIhIfeXk5NC/f38SEhIIBoMsXLiwtCc3JycHoNqe3fXr19O2bVvi4ur22M/YsWPLbefl5ZGTk8Mll1xCTMyBVUZ79uzJiSeeyMcff1yu/tlnn136Oi0tjYyMDE444QTatm1bWt6vXz8A1q5dW6fYKho3bhyFhYW8+eabPP/883Tu3Dli6xAXFRXxl7/8hX79+pGYmEhsbGxpr/n+4dzp6en06tWLO++8kyeeeILly5fXuZ2y739BQQEff/wx48aNw+fzUVJSQklJCc45zjjjDD755JMaz1Wf4xva/qhRo8ptH3nkkfzwww8A5Ofn89lnn/GjH/2IpKSq5zipT5sdO3Zk/fr1Nb4XjUXr7IpI3Vk42bUggSqS3YLiAIsCA+hx8k8g3bsJoOL8obV2t66cD8d3P7AjEHrW+JXY0dzhRWAiIlK1d+6EjYu8jaHzADj7/nodmpOTw+DBg4FQQpWXl1ea3M6fP5/27dvTo0ePKo/dt28f8fG1W7++rC5dupTb3rFjB865SuUAnTt3Zs2aNeXK0tLSym3HxcVVWbY/xoZISUnh/PPP59lnn+X777/nRz/6ET5f1X1vMTExBAKBKvftLy+bzFf029/+lkcffZS7776boUOHkpKSQm5uLhdccEHpz2FmzJo1iwkTJvDb3/6Wbdu20bNnT+644w5uvLF2j1+VfZ+3b99OIBDg3nvv5d57762yfjAYrPZnrs/xDW2/ffv25fbHx8eXvj87duwgGAxWuwRUfdtMTEykoCA6HQ5KdkWk7sLP7PoIUlLxmZaSIg7Z+w0rGEDX9t4+DxvfNfTMVNK2b4DzK+1PToiNckQiItJSBYNBli1bVjpJUMWe3FmzZjF06NBqj09PT680eVJtmFm57bS0NMyMjRs3Vqq7ceNG0tPT69xGY7ryyisZNWoUwWCQF154odp6GRkZbN26laKiokq93ft7BTt16lTt8S+++CJXXnklf/jDH0rL9k8UVlavXr2YOnUqzjkWLlzIP/7xD2666SZ69OhRrte7OmXf/3bt2uHz+bj55pu58sorq6xfXaJb3+Mbs/2K0tLS8Pl8lZ7zbmjM27dvp0OHDrWOoyGU7IpI3YWHMR9jK9i2t4iMlIQD+3aGPjEutIRK/wFHXecBQOVhzEU/zCUOSEv2boZoERGpQj17VJsCF/6/Zn8ilpOTQ48ePUhLS2PGjBnMmTOHadOmVXt8v379KC4uJjc3t8aetINJTk5m0KBBvPzyy0yYMAG/P/R/9po1a/j888+55ZZb6n3uxjBixAguvvhi2rVrxxFHHFFtvdNOO4377ruP6dOnc9FFF5XbN23aNLp06ULfvn2rPT4/P5/Y2PIfau+faboqZsbAgQN56KGHeOqpp/jmm29qleyWlZyczEknnVQ6fL0uiWVTOL6ipKQkhg0bxnPPPcfdd99NYmLlToz6tLl69erSERCRpmRXROqu/aEAdLIdFJVUPVvhJ7EnMiKaMdVg/c7yw672rF1EOpDU6wRvAhIRkRbH7/dz4YUX8uSTTxIIBJg9ezaJiYlcd911TJ06lRtuuIELLrig2uNPPvlkIDT7bUOSXYB7772XUaNGce6553LTTTexd+9exo8fT2pqKrfffnuDzt1Qfr+/xh7d/c444wxGjBjB1VdfzXfffcfxxx/Pnj17ePHFF3njjTdKl3WqzsiRI5kyZQoDBgygd+/evPrqq5WWu/n666+59dZbueSSS+jduzeBQIBnnnmGmJgYTj/99Hr9fA899BAnn3wyZ511Fj/96U/p0qULW7duJScnh0AgwP331/yBjtfHV/Tggw9yyimnMGTIEG6//XaysrJYtWoVCxYs4NFHH61zmzt37mTZsmX8+te/rlMc9aUJqkSk7pLTKYpPw2EUV7X0ENA9PTnKQVUl1LN8bsl75UqT5j8NQHznw6MekYiItFxTpkzh/vvvZ9GiRSxevJi8vDwAPvroIyZOnFjjsT169GDw4MG8+eabDY5j5MiRvP322+zcuZOLL76YG264gcMPP5z//ve/HHLIIQ0+fzSYGdOnT+e2225j6tSpnHvuuVx11VVs2LCB119/vdy6tFV59NFHGT16NL///e+55JJL2LNnT6Uku3PnznTr1o2HHnqI0aNHc9lll7F+/XreeustBg0aVK+4jz32WObOnUt6ejq/+MUvOPPMM7n11ltZtGhR6QcaTfn4io477jg+++wzunbtyi233MI555zDAw88UO4Dmbq0+fbbbxMXF1dpYrVIMVdxltJmLjs7282bN8/rMGqWOw+eHA5xKfC73IPXF2mCiu/ryQt5g0i58BHGHnPghhf8+mV8r17LlEPu4qrro/OpXbWCQbgnjS8C/cme8Bmxfh9sWQaPHQdA0R92EBejz/xERLzw7bffcvjhLfNDx5UrV9K7d2/eeecdRo4cWevjnnnmGW699VY2bNhQ7ey3Is3Z2WefTYcOHcqtt1yVg90fzOwr51z2wdrz9K88MxtpZkvNbIWZ3VnF/l+Z2RIz+9rMPjCz7lWdR0Siz+cCnOP/El+F53IL1i4AoDC9vxdhlefzkds2NDFI7o7QrH+BXaFnqf7ATUp0RUQkIg62zFB1rrjiCjIzM/nnP/8ZibBEPLVgwQI+/PBDxo8fH7U2PftLz8z8wGPA2UB/4DIzq/jX8Xwg2zl3FPAK8L/RjVJEquML7CONPZWe2S0odhS6GNJ7HOVRZOUlxfkY4l/C2m2hoWQ7Pn0cgB6HHe1lWCIi0oLl5OSQmZlJRkZGnY7z+/08/fTT6tWVFmnjxo1MnjyZ3r17R61NL7s1BgMrnHOrnHNFwIvAmLIVnHMfOufyw5uzgYY9rS8ijSb/mGspIpYVm8tP4799Tz5+giTF+T2KrLx4QmvqrvshtFB8hzUzADjh5LM8i0lERFq2++67j9zc+j2qdsIJJ3DTTTc1ckQi3hs5ciSXXXZZVNv0MtnNBNaW2c4Nl1Xnp8A7EY1IRGotKTaUzMZXGAp82MrJxFiQIw5J9SKsSnyDrwVgy+59uPnPA7Ao2IMjs9K8DEtEREREIszLZLeqBTirnC3LzH4MZAMPVLP/ejObZ2bztmzZ0oghikh19j+qu3j97nLle2PasTLYhYy28R5EVVlCbOg2137XN9gboU/KX+j2Ry9DEhEREZEo8DLZzQW6ltnOAtZXrGRmZwC/B0Y75wqrOpFzbpJzLts5l92xY8eIBCsilSVaEX5XVK6sOGjMoz8JsU1jGLNlhWZe7rDpMwDyXTw/v0hDmEVERERaOi+T3blAHzPraWZxwKXA9LIVzOwY4HFCie5mD2IUkWqFBmJkbXiP4r8eSsGfurFr5zYCQUeMvwnNctyxLwD7CkOflf2x5EoOaZfoZUQiIiIiEgWe/UXqnCsBfg7MBL4FXnLOLTaze8xsdLjaA0Ab4GUzW2Bm06s5nYhE28AfAXBMwWxiC7aSWLKL1Ed60YGdtE+o6ikFbwUCJQD07tTW40hEREREJBpivGzcOTcDmFGh7O4yr8+IelAiUkuhhPY8/+xKewoPOT7awRzUhf7/AtClgyamEhFpKpxzmDW9D0hFxDvOVTmNU700obGGrUhMeOKezkd6G4dIQ6R0qnbXwLOujGIgddP/9Mu9DkFERICYmBhKSkq8DkNEmpiSkhJiYhqnT9bTnt1Wq9ORcPlL0HmA15GI1F9iGi69N7ZtBZsG3ECnU6+HLUshNYvOGRleR1feb9aQ/+rP2R3fhV6d072ORkREgISEBPbu3UtamkbciMgBe/bsISEhoVHOZY3ZTdwUZGdnu3nz5nkdhkjrUJQPP3wO3YdBbOPclEREpHXYt28fP/zwA1lZWSQmJmo4s0gr55yjoKCA3NxcunXrVmPCa2ZfOeeyD3ZO9eyKSP3FJUFvPVovIiJ1l5CQQKdOndi4cSOFhVWuLikirUx8fDydOnVqtJ5dJbsiIiIi4onU1FRSU1O9DkNEWqiDTlBlZh3N7HEzeyu83d/Mro54ZCIiIiIiIiL1VJvZmJ8BPga6hreXA7dHKiARERERERGRhqpNspvhnPsXEARwzhUDgYhGJSIiIiIiItIAtUl288ysPeAAzOw4YE9EoxIRERERERFpgNpMUHUH8CbQy8w+BjKBcRGNSkRERERERKQBapPszgdOAw4HDFhCeEiziIiIiIiISFNUm2HMc5xzRc65hc65Bc65ImBOpAMTERERERERqa9qe3bNLAPoAiSa2QBCvboAbYGkKMQmIiIiIiIiUi81DWMeBVwDZAH/LFO+B7grkkGJiIiIiIiINES1ya5zbjIw2cwuds69FMWYRERERERERBrkoBNUOedeMrOzgCOAhDLlf4lkYCIiIiIiIiL1ddBk18z+CbQDTgYmAxcCsyMcl4iIiIiIiEi91WY25mHOucuBbc65u4DjCT3HKyIiIiIiItIk1SbZLQh/32dmnYF9QI+IRSQiIiIiIiLSQAcdxgy8Y2btgAeBBUAAmBrRqEREREREREQaoDYTVE0Iv3zZzN4CEp1z2yMalYiIiIiIiEgD1GYYcynnXAFwtJm9E6F4RERERERERBqs2mTXzE4xsyVmttPMnjGzvmY2G3iE0KzMDWZmI81sqZmtMLM7q9gfb2b/Du//0sx6NEa7IiIiIiIi0rLV1LP7CPALIBN4C5gDvOScO9o591JDGzYzP/AYcDbQH7jMzPpXqPZTYIdzrjfwMPDXhrYrIiIiIiIiLV+Nw5idc+875/Kcc68A2wglnI1lMLDCObfKOVcEvAiMqVBnDDAl/PoVYLiZWSPGICIiIiIiIi1QTRNUpZrZ6Apl5+3PNZ1z0xvYdiawtsx2LqE1fKus45wrMbNdQDqwtYFti4iIiIiISAtWU7L7GTCumm0HNDTZraqH1tWjDmZ2PXA9QLdu3RoYloiIiIiIiDR31Sa7zrkrItx2LtC1zHYWsL6aOrlmFgOkApWWPXLOTQImAWRnZ1dKhkVERERERKR1qdPSQ41sLtDHzHqaWRxwKZV7i6cDV4VfXwT8xzmnZFZERERERERqVNMw5ogKP4P7c2Am4Aeeds4tNrN7gHnhZ4KfAp41sxWEenQv9Y/Sq/4AAA/8SURBVCpeERERERERaT48S3YBnHMzgBkVyu4u83of5Z8bFhERERERETmogya7ZvYh8AnwKfC5cy4/4lGJiIiIiIiINEBtntn9GbAG+BEwz8y+NLMHIhuWiIiIiIiISP0dtGfXObfMzHYCu8NfZwHHRDowERERERERkfo6aM+umS0F3gS6A88DRzrnzoh0YCIiIiIiIiL1VZthzJMIrX97EXA9cJmZdY9oVCIiIiIiIiINcNBk1zn3/5xzY4HhwELgT8CqSAcmIiIiIiIiUl+1mY35r8AwIB2YDdxDaGZmERERERERkSapNuvsLgD+7pxbF+lgRERERERERBpDbWZjfsHMUs3sWCChTPnnEY1MREREREREpJ5qM4z5GuB2IBNYBBxHaDjzqRGNTERERERERKSeajMb8y+BbOB759xJwCBgQ0SjEhEREREREWmA2iS7+5xzBQBmFuecWwz0i2xYIiIiIiIiIvVXmwmqNphZO+BNYKaZbQc2RTYsERERERERkfqrNtk1sxnATc650eGiu8xsOJAKvB2N4ERERERERETqo6ae3WeA98zsGeAB51yxc+6DqEQlIiIiIiIi0gDVJrvOuZfM7G3gbmCemT0LBMvsfygK8YmIiIiIiIjU2cGe2S0G8oB4IIUyya6IiIiIiIhIU1XTM7sjgYeA6cCxzrn8qEUlIiIiIiIi0gA19ez+HhgXXmpIREREREREpNmo6Zndk6IZiIiIiIiIiEhj8XkdgIiIiIiIiEhjU7IrIiIiIiIiLY6SXREREREREWlxPEl2zay9mc0ys+Xh72lV1BloZl+Y2WIz+9rMLvEiVhEREREREWl+vOrZvRP4wDnXB/ggvF1RPnClc+4IYCTwiJm1i2KMIiIiIiIi0kx5leyOAaaEX08Bzq9YwTm3zDm3PPx6PbAZ6Bi1CEVERERERKTZ8irZ7eSc2wAQ/p5RU2UzGwzEASujEJuIiIiIiIg0c9Wus9tQZvY+0LmKXb+v43m6AM8CVznngtXUuR64HqBbt251jFRERERERERamoglu865M6rbZ2abzKyLc25DOJndXE29tsDbwB+cc7NraGsSMAkgOzvbNSxyERERERERae68GsY8Hbgq/Poq4I2KFcwsDngNmOqcezmKsYmIiIiIiEgz51Wyez8wwsyWAyPC25hZtpk9Ga5zMXAycLWZLQh/DfQmXBEREREREWlOzLmWNeo3OzvbzZs3z+swREREREREJALM7CvnXPbB6nnVsysiIiIiIiISMUp2RUREREREpMVRsisiIiIiIiItjpJdERERERERaXGU7IqIiIiIiEiLo2RXREREREREWhwluyIiIiIiItLiKNkVERERERGRFkfJroiIiIiIiLQ4SnZFRERERESkxVGyKyIiIiIiIi2Okl0RERERERFpcZTsioiIiIiISIujZFdERERERERaHCW7IiIiIiIi0uIo2RUREREREZEWR8muiIiIiIiItDhKduX/t3f/sXdX9R3Hny/a4cCOUcZAVmRFVlHG5tBvkB/RGCj4Y4tlCSYYMsvWhWy4yZaNrc4/zOaS1eB0I06yDpjFMVFLM5rOKbUjbjLHKJZRoJI24KCsAyc/BDVq8b0/Pqd6vfuWFvq9936/nz4fyc39nPM599xzv333fO/7e879XEmSJEnqHZNdSZIkSVLvmOxKkiRJknrHZFeSJEmS1Dsmu5IkSZKk3plIspvkqCQbk2xv9wufo+0RSR5J8uFxjlGSJEmSNHdNamV3JbCpqpYAm1p5b94HfH4so5IkSZIk9cKkkt1lwJp2vAa4YLpGSV4DHAvcMqZxSZIkSZJ6YFLJ7rFVtQug3R8z3CDJIcCfA1eMeWySJEmSpDlu/qg6TvI54CXTnHrPfnZxGfDpqno4yb6e61LgUoATTjjh+QxTkiRJktRDI0t2q2rp3s4leTTJcVW1K8lxwGPTNDsTeF2Sy4AFwKFJnqmq//f53qpaDawGmJqaqpl5BZIkSZKkuWpkye4+rAeWA6va/c3DDarq4j3HSS4BpqZLdCVJkiRJGjapz+yuAs5Lsh04r5VJMpXkmgmNSZIkSZLUE6nq167fqamp2rx586SHIUmSJEkagSR3VtXUvtpNamVXkiRJkqSRMdmVJEmSJPWOya4kSZIkqXdMdiVJkiRJvWOyK0mSJEnqHZNdSZIkSVLvmOxKkiRJknrHZFeSJEmS1Dsmu5IkSZKk3jHZlSRJkiT1jsmuJEmSJKl3THYlSZIkSb1jsitJkiRJ6h2TXUmSJElS75jsSpIkSZJ6x2RXkiRJktQ7JruSJEmSpN4x2ZUkSZIk9Y7JriRJkiSpd0x2JUmSJEm9Y7IrSZIkSeodk11JkiRJUu+Y7EqSJEmSemciyW6So5JsTLK93S/cS7sTktySZFuS+5IsHu9IJUmSJElz0aRWdlcCm6pqCbCpladzPXBlVb0SOB14bEzjkyRJkiTNYZNKdpcBa9rxGuCC4QZJTgHmV9VGgKp6pqq+Ob4hSpIkSZLmqkklu8dW1S6Adn/MNG1eDjyZZF2SLUmuTDJvrKOUJEmSJM1J80fVcZLPAS+Z5tR79rOL+cDrgNOAh4BPAJcA107zXJcCl7biM0nuf77jHbOjgf+d9CDUO8aVRsXY0igYVxoF40qjYmzNLj+9P41GluxW1dK9nUvyaJLjqmpXkuOY/rO4O4EtVfVAe8w/AGcwTbJbVauB1TMz8tFLsrmqpiY9DvWLcaVRMbY0CsaVRsG40qgYW3PTpLYxrweWt+PlwM3TtLkDWJjkJ1v5HOC+MYxNkiRJkjTHTSrZXQWcl2Q7cF4rk2QqyTUAVfUs8PvApiRbgQB/M6HxSpIkSZLmkJFtY34uVfU14Nxp6jcDvz5Q3gj8/BiHNi5zZsu15hTjSqNibGkUjCuNgnGlUTG25qBU1aTHIEmSJEnSjJrUNmZJkiRJkkbGZHfMkrwpyf1JdiRZOenxaHZL8tIktybZluTeJJe3+qOSbEyyvd0vbPVJclWLr7uTvHqgr+Wt/fYky/f2nDp4JJnXvsd8QyufmOT2FiOfSHJoq39RK+9o5xcP9PHuVn9/kjdO5pVotkhyZJK1Sb7c5q0zna90oJL8bvsdeE+Sjyf5UecrvRBJrkvyWJJ7BupmbI5K8pokW9tjrkqS8b5CDTPZHaMk84C/At4MnAK8Pckpkx2VZrndwO9V1SvpvnrrnS1mVgKbqmoJsKmVoYutJe12KXA1dBM58F7gtcDpwHv3TOY6qF0ObBsovx/4UIurJ4AVrX4F8ERV/QzwodaOFosXAT8LvAn4SJvndPD6S+AzVfUK4FV08eV8pRcsySLgXcBUVZ0KzKObd5yv9EJ8lO7ff9BMzlFXt7Z7Hjf8XBozk93xOh3YUVUPVNV3gBuBZRMek2axqtpVVV9qx0/TvXFcRBc3a1qzNcAF7XgZcH11/h04Mt13Wb8R2FhVj1fVE8BGnIAPakmOB34RuKaVQ/cVb2tbk+G42hNva4FzW/tlwI1V9e2qehDYQTfP6SCU5Ajg9cC1AFX1nap6EucrHbj5wGFJ5gOHA7twvtILUFX/Ajw+VD0jc1Q7d0RVfbG6iyJdP9CXJsRkd7wWAQ8PlHe2Ommf2las04DbgWOrahd0CTFwTGu2txgz9jTsL4A/AL7Xyj8BPFlVu1t5MEa+Hz/t/FOtvXGlQS8Dvgr8bdsef02SF+N8pQNQVY8AHwAeoktynwLuxPlKM2em5qhF7Xi4XhNksjte0+3b93LY2qckC4CbgN+pqq8/V9Np6uo56nUQSvJLwGNVdedg9TRNax/njCsNmg+8Gri6qk4DvsEPtgNOx7jSPrXtocuAE4GfAl5Mt710mPOVZtrzjSVjbBYy2R2vncBLB8rHA/89obFojkjyI3SJ7g1Vta5VP9q2y9DuH2v1e4sxY0+DzgbemuQrdB+nOIdupffItk0QfjhGvh8/7fyP020DM640aCews6pub+W1dMmv85UOxFLgwar6alV9F1gHnIXzlWbOTM1RO9vxcL0myGR3vO4AlrQrCB5Kd6GE9RMek2ax9jmja4FtVfXBgVPrgT1X/1sO3DxQ/452BcEzgKfalpzPAucnWdj+Sn5+q9NBqKreXVXHV9Viunnon6vqYuBW4MLWbDiu9sTbha19tfqL2tVPT6S7GMd/jOllaJapqv8BHk5ycqs6F7gP5ysdmIeAM5Ic3n4n7okr5yvNlBmZo9q5p5Oc0WL1HQN9aULm77uJZkpV7U7yW3T/SeYB11XVvRMelma3s4FfAbYmuavV/RGwCvhkkhV0bwTe1s59GngL3YU3vgn8KkBVPZ7kfXR/cAH4k6oavkCD9IfAjUn+FNhCu9BQu/9Ykh10KyQXAVTVvUk+SffGczfwzqp6dvzD1izy28AN7Q+6D9DNQYfgfKUXqKpuT7IW+BLdPLMFWA38I85Xep6SfBx4A3B0kp10V1WeyfdUv0l3xefDgH9qN01Quj92SZIkSZLUH25jliRJkiT1jsmuJEmSJKl3THYlSZIkSb1jsitJkiRJ6h2TXUmSJElS75jsSpI0QkmeTXLXwG1xkqkkV7XzlyT5cDu+IMkpB/h8hye5IcnWJPck+UKSBUmOTHLZTLwmSZLmAr9nV5Kk0fpWVf3CUN1XgM3TtL0A2ED3XaD7Jcn8qto9UHU58GhV/Vw7fzLwXeBo4DLgI/s/dEmS5i5XdiVJGrMkb0iyYajuLOCtwJVtBfikdvtMkjuT/GuSV7S2H03ywSS3Au8f6v444JE9haq6v6q+DawCTmp9X9n6uSLJHUnuTvLHrW5xki8nWdPq1yY5fGQ/DEmSRsSVXUmSRuuwJHe14wer6pena1RV/5ZkPbChqtYCJNkE/EZVbU/yWrpV2XPaQ14OLK2qZ4e6ug64JcmFwCZgTVVtB1YCp+5ZZU5yPrAEOB0IsD7J64GHgJOBFVV1W5Lr6FaEP3DgPwpJksbHZFeSpNGabhvzPiVZAJwFfCrJnuoXDTT51DSJLlV1V5KXAecDS4E7kpwJfGuo6fnttqWVF9Alvw8BD1fVba3+74B3YbIrSZpjTHYlSZqdDgGefI5E+Rt7e2BVPQOsA9Yl+R7wFuCmoWYB/qyq/vqHKpPFQA13uf/DliRpdvAzu5IkzR5PAz8GUFVfBx5M8jaAdF61rw6SnJ1kYTs+FDgF+K/BvpvPAr/WVpBJsijJMe3cCW01GODtwBcO+JVJkjRmJruSJM0eNwJXJNmS5CTgYmBFkv8E7gWW7UcfJwGfT7KVbovyZuCmqvoacFv7OqIrq+oW4O+BL7a2a/lBMrwNWJ7kbuAo4OoZfI2SJI1FqtyZJEmSOm0b84aqOnXCQ5Ek6YC4sitJkiRJ6h1XdiVJkiRJvePKriRJkiSpd0x2JUmSJEm9Y7IrSZIkSeodk11JkiRJUu+Y7EqSJEmSesdkV5IkSZLUO/8HDnmb/UmHu4EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotx()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Position x/y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#%pylab --no-import-all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plotxy():\n",
    "\n",
    "    fig = plt.figure(figsize=(16,9))\n",
    "\n",
    "    # EKF State\n",
    "    plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600', units='xy', width=0.05, scale=0.5)\n",
    "    plt.plot(x0,x1, label='EKF Position', c='k', lw=5)\n",
    "\n",
    "    # Measurements\n",
    "    plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', marker='+')\n",
    "    #cbar=plt.colorbar(ticks=np.arange(20))\n",
    "    #cbar.ax.set_ylabel(u'EPE', rotation=270)\n",
    "    #cbar.ax.set_xlabel(u'm')\n",
    "\n",
    "    # Start/Goal\n",
    "    plt.scatter(x0[0],x1[0], s=60, label='Start', c='g')\n",
    "    plt.scatter(x0[-1],x1[-1], s=60, label='Goal', c='r')\n",
    "\n",
    "    plt.xlabel('X [m]')\n",
    "    plt.ylabel('Y [m]')\n",
    "    plt.title('Position')\n",
    "    plt.legend(loc='best')\n",
    "    plt.axis('equal')\n",
    "    #plt.tight_layout()\n",
    "\n",
    "    #plt.savefig('Extended-Kalman-Filter-CTRV-Position.png', dpi=72, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WjhGLSLmXkQHDhkFwMEREwMmTmb8GB8Pw4Zed3K5evZoqVaowduzYnLYmTZrw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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b2fc7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotxy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Detailed View"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plotxydetails():\n",
    "    fig = plt.figure(figsize=(12,9))\n",
    "\n",
    "    plt.subplot(221)\n",
    "    # EKF State\n",
    "    #plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600', units='xy', width=0.05, scale=0.5)\n",
    "    plt.plot(x0,x1, label='EKF Position', c='g', lw=5)\n",
    "\n",
    "    # Measurements\n",
    "    plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', alpha=0.5, marker='+')\n",
    "    #cbar=plt.colorbar(ticks=np.arange(20))\n",
    "    #cbar.ax.set_ylabel(u'EPE', rotation=270)\n",
    "    #cbar.ax.set_xlabel(u'm')\n",
    "\n",
    "    plt.xlabel('X [m]')\n",
    "    plt.xlim(70, 130)\n",
    "    plt.ylabel('Y [m]')\n",
    "    plt.ylim(140, 200)\n",
    "    plt.title('Position')\n",
    "    plt.legend(loc='best')\n",
    "\n",
    "\n",
    "    plt.subplot(222)\n",
    "\n",
    "    # EKF State\n",
    "    #plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600', units='xy', width=0.05, scale=0.5)\n",
    "    plt.plot(x0,x1, label='EKF Position', c='g', lw=5)\n",
    "\n",
    "    # Measurements\n",
    "    plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', alpha=0.5, marker='+')\n",
    "    #cbar=plt.colorbar(ticks=np.arange(20))\n",
    "    #cbar.ax.set_ylabel(u'EPE', rotation=270)\n",
    "    #cbar.ax.set_xlabel(u'm')\n",
    "\n",
    "    plt.xlabel('X [m]')\n",
    "    plt.xlim(160, 260)\n",
    "    plt.ylabel('Y [m]')\n",
    "    plt.ylim(110, 160)\n",
    "    plt.title('Position')\n",
    "    plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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48GBWr14NwMqVK3nsscey+tLS0pg4cSIBAQE0a9aMTz/9FIDr16/TpUsXWrRoQdOmTfnx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf31110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotxydetails()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "As you can see, complicated analytic calculation of the Jacobian Matrices, but it works pretty well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Write Google Earth KML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Convert back from Meters to Lat/Lon (WGS84)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "latekf = latitude[0] + np.divide(x1,arc)\n",
    "lonekf = longitude[0]+ np.divide(x0,np.multiply(arc,np.cos(latitude*np.pi/180.0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Create Data for KML Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Coordinates and timestamps to be used to locate the car model in time and space\n",
    "The value can be expressed as yyyy-mm-ddThh:mm:sszzzzzz, where T is the separator between the date and the time, and the time zone is either Z (for UTC) or zzzzzz, which represents ±hh:mm in relation to UTC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "car={}\n",
    "car['when']=[]\n",
    "car['coord']=[]\n",
    "car['gps']=[]\n",
    "for i in range(len(millis)):\n",
    "    d=datetime.datetime.fromtimestamp(millis[i]/1000.0)\n",
    "    car[\"when\"].append(d.strftime(\"%Y-%m-%dT%H:%M:%SZ\"))\n",
    "    car[\"coord\"].append((lonekf[i], latekf[i], 0))\n",
    "    car[\"gps\"].append((longitude[i], latitude[i], 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from simplekml import Kml, Model, AltitudeMode, Orientation, Scale, Style, Color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# The model path and scale variables\n",
    "car_dae = r'https://raw.githubusercontent.com/balzer82/Kalman/master/car-model.dae'\n",
    "car_scale = 1.0\n",
    "\n",
    "# Create the KML document\n",
    "kml = Kml(name=d.strftime(\"%Y-%m-%d %H:%M\"), open=1)\n",
    "\n",
    "# Create the model\n",
    "model_car = Model(altitudemode=AltitudeMode.clamptoground,\n",
    "                            orientation=Orientation(heading=75.0),\n",
    "                            scale=Scale(x=car_scale, y=car_scale, z=car_scale))\n",
    "\n",
    "# Create the track\n",
    "trk = kml.newgxtrack(name=\"EKF\", altitudemode=AltitudeMode.clamptoground,\n",
    "                     description=\"State Estimation from Extended Kalman Filter with CTRV Model\")\n",
    "\n",
    "# Attach the model to the track\n",
    "trk.model = model_car\n",
    "trk.model.link.href = car_dae\n",
    "\n",
    "# Add all the information to the track\n",
    "trk.newwhen(car[\"when\"])\n",
    "trk.newgxcoord(car[\"coord\"])\n",
    "\n",
    "# Style of the Track\n",
    "trk.iconstyle.icon.href = \"\"\n",
    "trk.labelstyle.scale = 1\n",
    "trk.linestyle.width = 4\n",
    "trk.linestyle.color = '7fff0000'\n",
    "\n",
    "# Add GPS measurement marker\n",
    "fol = kml.newfolder(name=\"GPS Measurements\")\n",
    "sharedstyle = Style()\n",
    "sharedstyle.iconstyle.icon.href = 'http://maps.google.com/mapfiles/kml/shapes/placemark_circle.png'\n",
    "\n",
    "for m in range(len(latitude)):\n",
    "    if GPS[m]:\n",
    "        pnt = fol.newpoint(coords = [(longitude[m],latitude[m])])\n",
    "        pnt.style = sharedstyle\n",
    "\n",
    "# Saving\n",
    "#kml.save(\"Extended-Kalman-Filter-CTRV.kml\")\n",
    "kml.savekmz(\"Extended-Kalman-Filter-CTRV.kmz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported KMZ File for Google Earth\n"
     ]
    }
   ],
   "source": [
    "print('Exported KMZ File for Google Earth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To use this notebook as a presentation type:\n",
    "\n",
    "`jupyter-nbconvert --to slides Extended-Kalman-Filter-CTRV.ipynb --reveal-prefix=reveal.js --post serve` \n",
    "\n",
    "Questions? [@Balzer82](https://twitter.com/balzer82)"
   ]
  }
 ],
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